Advances in Acoustic Emission Technology: Proceedings of the World Conference on Acoustic Emission-2017 [1st ed.] 978-3-030-12110-5;978-3-030-12111-2

Table of contents :
Front Matter ....Pages i-xiv
Front Matter ....Pages 1-1
Discussion on Elastic Wave Sources for AE Sensor Calibration at Low Frequency (Longbiao He, Min Wang, Jian Kang, Ping Yang)....Pages 3-8
Proposal for an Absolute AE Sensor Calibration Setup (Hartmut Vallen)....Pages 9-24
Difference Between FBG and PZT Acoustic Emission Sensor (Dong Xia, Peng Wei, Chenggui Li)....Pages 25-36
Research on Fiber Bragg Grating Acoustic Emission Sensor (Yang Yu, Guoliang Wang, Bo Liu)....Pages 37-48
The Optimization of Magneto Acoustic Emission Testing Device (Yongna Shen, Gongtian Shen, Wenjun Zhang)....Pages 49-55
Power Over Ethernet Daisy Chained Acoustic Emission System for Structure Health Monitoring (E. Lowenhar, J. Dong)....Pages 57-64
UNISCOPE: Instrument Integrating NDT Methods (S. Elizarov, V. Bardakov, A. Shimanskiy, A. Alyakritskiy, D. Terentyev, V. Barat et al.)....Pages 65-74
Fiber Bragg Grating Acoustic Emission Demodulation System (Yang Yu, Xiangyin Bu, Bo Liu, Ping Yang)....Pages 75-83
Front Matter ....Pages 85-85
Application of Modal Acoustic Emission Technique for Recognition of Corrosion Severity on a Thin Plate (Weigang Zhang, Jie Geng, Yanting Xu)....Pages 87-99
Research on the Identification of Crack Status Through the Axle Acoustic Emission Signal Based on Local Mean Decomposition and Grey Correlation Analysis (Lin Li, Wang Huawei, Zhou Yong)....Pages 101-117
Acoustic Emission Characteristics Based on Energy Mode of IMFs (Aijun Gu, Linsong Sun, Jindong Liang, Wenqin Han)....Pages 119-129
Two-Dimensional Source Location of Acoustic Emission by Means of AI (H. Asaue, T. Shiotani, K. Hashimoto)....Pages 131-138
Front Matter ....Pages 139-139
Acoustic Emission RA-Value and Granite Fracture Modes Under Dynamic and Static Loads (Xiling Liu, Zhou Liu, Xibing Li, Jiahui Cui)....Pages 141-153
Damage Detection in Glass Fiber-Reinforced Plastics Using Ultrasonic Full-Waveform Comparison (Qiang Wang, Thomas Schumacher, Ali Hafiz)....Pages 155-163
Fatigue Damage Evaluation of 2.25Cr-1Mo-0.25V Steel Using Acoustic Emission Entropy (Mengyu Chai, Jinghai Xiang, Zongqi Zhao, Zaoxiao Zhang, Quan Duan)....Pages 165-175
Acoustic Emission Behavior of TC4 Titanium Alloy Manufactured by Electron-Beam Free-Form Fabrication During Tensile Deformation (Zhanwen Wu, Gongtian Shen, Yongna Shen, Junjiao Zhang, Ran Liu)....Pages 177-182
An Entropy Approach for Characterization and Assessment of Fatigue Damage Accumulation in Q235 Steel Based on Acoustic Emission Testing (Zhonghui Jia, Jianyu Li, Gang Qi)....Pages 183-194
Tensile Deformation Damage and Clustering Analysis of Acoustic Emission Signals in Three-Dimensional Woven Composites (W. Zhou, Y. N. Zhang, W. Z. Zhao)....Pages 195-204
The Study of Mechanical Behavior of Alloy Structural Steel Based on Dynamic Acoustic Emission Signal (Xiaoli Li, Xinbo Chen, Jinli Sun)....Pages 205-214
Acoustic Emission Characteristic of Ceramic Matrix Composite Under Static Loading (Yong Gao, Denghong Xiao, Liang Jin, Bo Jiang, Naitian Li, Quanhong Ye et al.)....Pages 215-223
Front Matter ....Pages 225-225
Evaluation of Damage in RC Bridge Decks Reinforced with Steel Plates by AE Tomography (Yiming Feng, Tomoki Shiotani, Yoshikazu Kobayashi, Takahiro Nishida, Hisafumi Asaue, Katsufumi Hashimoto et al.)....Pages 227-237
Damage Quantification Using an Improved b-Value for Concrete Slabs (T. Shiotani, C. Granier, K. Hashimoto)....Pages 239-247
Defect Diagnosis of Low-Speed Heavy-Duty Bearings Using Acoustic Emission (Guanghai Li, Yang Jiao, Zhanwen Wu)....Pages 249-257
Investigation on Acoustic Emission Characteristics of Steel Structure of Amusement Device (Junjiao Zhang, Gongtian Shen, Zhanwen Wu, Yilin Yuan, Ran Liu)....Pages 259-268
Case Studies on Tank Bottom In-Service Acoustic Emission Testing and Its Verifications (Yewei Kang, Zhenghong Guo, Yi Zhang, Huatian Xu)....Pages 269-277
Corrosion Degree Evaluation and Leakage Judgment of Vertical Storage Tanks by AE Test (Yanting Xu, Yadong Wang, Weigang Zhang)....Pages 279-288
Acoustic Emission Testing and Evaluation of Ethylene Horizontal Tank (Yadong Wang, Zhi Xiang, Yanting Xu, Jiele Xu, Zhongteng Lai)....Pages 289-295
Application and Research of Acoustic Emission in the Fatigue Test of Hoop-Wrapped Composite Cylinders (Yaping Liu, Gongtian Shen, Yadi Yan, Yang Li, Yong Zhang)....Pages 297-304
A Method for Small Leak Precise Location in Pressure Piping by Acoustic Emission (Ni Qin, Yongmei Hao, Xinming Yan, Yunfei Yue)....Pages 305-315
Study of Acoustic Emission Attenuation Characteristics of the Steel Bifurcated Pipe in Hydropower Station (Weiping Wu, Shengjin Cheng, Dongfeng Li, Shulin Cao, Bo Lü)....Pages 317-326
Research on Pipeline Fault Diagnosis Technology Based on Automatic Encoder (Xinying Wang, Xingshuai Song, Taiwang Yang, Huiran Zhang, Haiqun Chen)....Pages 327-339
Acoustic Emission Testing of a Friction Stir Welding Aluminum Alloy Pressure Vessel (Jun Jiang, Cheng Ye, Zhongzheng Zhang, Yongliang Yu)....Pages 341-351
Front Matter ....Pages 353-353
Valve Leakage Analysis in Reciprocating Compressor by Using Acoustic Emission Technique (H. Y. Sim, R. Ramli, A. Saifizul)....Pages 355-363
Determination of Characteristic Frequency Segments of Acoustic Emission Signal for Valve Leakage Detection in Reciprocating Compressor (R. Ramli, H. Y. Sim, A. Saifizul)....Pages 365-377
Acoustic Emission Fault Diagnosis of Rolling Bearing Based on Discrete Hidden Markov Model (Fuping Guo, Shuqian Shen, Zhihong Duan, Zhiqing Fan, Zhiwei Sun)....Pages 379-386
Front Matter ....Pages 387-387
Influencing Factors of Partial Discharge of Needle-Plate Based on Acoustic Emission Detection (Yu Zhang, Longbiao He, Haijiang Zhu)....Pages 389-397
Application of Multistep Source Localization Method with Narrowing Velocity Interval in Mines (Longjun Dong, Daoyuan Sun, Weiwei Shu, Xibing Li, Jian Wang)....Pages 399-408
Statistical Precursor of Induced Seismicity Using Temporal and Spatial Characteristics of Seismic Sequence in Mines (Longjun Dong, Daoyuan Sun, Weiwei Shu, Xibing Li, Lingyun Zhang)....Pages 409-420
A Method for Leak Detection of Spacecraft in Orbit Based on Beam-Forming (Lei Qi, Lichen Sun, Donghui Meng, Yong Wang, Wei Sun, Rongxin Yan)....Pages 421-427
Improved Ray Tracing Method Based on the Snell’s Law (Qingchun Hu)....Pages 429-442
Back Matter ....Pages 443-451

Citation preview

Springer Proceedings in Physics 218

Gongtian Shen Junjiao Zhang Zhanwen Wu Editors

Advances in Acoustic Emission Technology Proceedings of the World Conference on Acoustic Emission-2017

Springer Proceedings in Physics Volume 218

The series Springer Proceedings in Physics, founded in 1984, is devoted to timely reports of state-of-the-art developments in physics and related sciences. Typically based on material presented at conferences, workshops and similar scientific meetings, volumes published in this series will constitute a comprehensive up-to-date source of reference on a field or subfield of relevance in contemporary physics. Proposals must include the following: – – – – –

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More information about this series at http://www.springer.com/series/361

Gongtian Shen • Junjiao Zhang • Zhanwen Wu Editors

Advances in Acoustic Emission Technology Proceedings of the World Conference on Acoustic Emission-2017

Editors Gongtian Shen China Special Equipment Inspection and Research Institute Beijing, China

Junjiao Zhang China Special Equipment Inspection and Research Institute Beijing, China

Zhanwen Wu China Special Equipment Inspection and Research Institute Beijing, China

World Conference on Acoustic Emission http://182.92.158.112:99/www.wcacousticemission.org/call_for_paper.php ISSN 0930-8989 ISSN 1867-4941 (electronic) Springer Proceedings in Physics ISBN 978-3-030-12110-5 ISBN 978-3-030-12111-2 (eBook) https://doi.org/10.1007/978-3-030-12111-2 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This volume includes the papers selected from the World Conference on Acoustic Emission, 2017 (WCAE-2017), which was held on October 10 to October 13 in Xi’an, China. WCAE was the formal conference of the International Society on Acoustic Emission (ISAE). WCAE-2017 was jointly organized by Chinese Society for Non-destructive Testing (ChSNDT) and China Special Equipment Inspection and Research Institute (CSEI). This was the fourth such event of ISAE following the inaugural one in 2011 in Beijing; second in 2013 in Shanghai, China; and third in 2015 in Hawaii, USA. WCAE is aimed at providing a platform to scientists and practitioners in both academia and industry in the field of acoustic emission and exchanging research and application information, with particular emphasis on scientific and technical development and cooperation worldwide. The WCAE-2017 consisted of four invited keynote lectures and nine oral and one poster sessions. It covered all major areas of acoustic emission applications, including instrumentation, signal processing and analysis, material characteristics, structure, condition monitoring and diagnosis, and miscellaneous. WCAE-2017 received 56 submissions, which were documented in the extended abstracts of WCAE-2017. Forty-seven full papers were submitted for the consideration of achieved formal proceeding. After peer reviews, 40 were included in this volume. We are grateful to all the members of the committees who contributed their efforts to review the manuscripts. We would also like to express our sincere gratitude to Chinese Society for Non-destructive Testing (ChSNDT) and China Special Equipment Inspection and Research Institute (CSEI) for their enormous supports. Last but not the least, we thank the contributions of all the delegates who participated in WCAE-2017. Beijing, China

Gongtian Shen Junjiao Zhang Zhanwen Wu

v

Committees

Organization Committee Chair:

Gongtian Shen, China Special Equipment Inspection and Research Institute Co-chair: Allen Green, Acoustic Emission Technology Consulting Gary Qi, University of Memphis Members: Yongchang Xu, Chinese Society for Non-destructive Testing Bangxian Li, China Special Equipment Inspection and Research Institute Zhen Huo, Wuhan Boiler Pressure Vessel Inspection Institute Wei Li, Northeast Petroleum University Weihe Guan, Hefei General Machinery Research Institute Zhejun Liu, Aerospace Research Institute of Materials and Processing Technology Yanting Xu, Zhejiang Provincial Special Equipment Inspection and Research Institute Guanghai Li, China Special Equipment Inspection and Research Institute Zhanwen Wu, China Special Equipment Inspection and Research Institute

Paper and Program Committee Chair:

Gongtian Shen, China Special Equipment Inspection and Research Institute Co-chair: Gary Qi, University of Memphis Members: Allen Green, Acoustic Emission Technology Consulting Manabu Enoki, University of Tokyo vii

viii

Committees

Oswaldo Santos Filho, Technology Center, Eletronorte Karen Holford, Cardiff University Andy C. C. Tan, Queensland University of Technology Peter Tscheliesnig, TÜV Austria Services GmbH Bangxian Li, China Special Equipment Inspection and Research Institute Cherdpong Jomdecha, King Mongkut’s University of Technology Boris Muravin, Association of Engineers and Architects in Israel

Secretary Committee Chair:

Bangxian Li, China Special Equipment Inspection and Research Institute Members: Zhanwen Wu, China Special Equipment Inspection and Research Institute Yaqing Zhu, Chinese Society for Non-destructive Testing Yingyun Wang, Chinese Society for Non-destructive Testing Jingyuan Ji, Chinese Society for Non-destructive Testing Guanghai Li, China Special Equipment Inspection and Research Institute Junjiao Zhang, China Special Equipment Inspection and Research Institute

Advisory Committee Gary Qi, University of Memphis, United States of America Allen Green, Acoustic Emission Technology Consulting, United States of America Gongtian Shen, China Special Equipment Inspection and Research Institute, China Manabu Enoki, University of Tokyo, Japan Karen Holford, Cardiff University, United Kingdom Oswaldo Santos Filho, Eletrobras Eletronorte, Brazil Bangxian Li, China Special Equipment Inspection and Research Institute, China Cherdpong Jomdecha, King Mongkut’s University of Technology, Thailand Andy Tan, Queensland University of Technology, Australia Boris Muravin, Association of Engineers and Architects in Israel, Israel Rongsheng Geng, Beijing Aeronautical Technology Research Center, China

Committees

ix

Tomoki Shiotani, Kyoto University, Japan David E. Kosnik, CTL Group, United States of America Rickard Nordstrom, Richard Nordstrom Consulting, United States of America Oleg Bashkov, Komsomolsk-on-Amur State Technical University, Russia Gabor Por, University of Dunaujvaros, Hungary Jason Dong, MISTRAS Group, Inc., United States of America Shigenori Yuyama, Nippon Physical Acoustics, LTD., Japan Martin Browne, University of Southampton, United Kingdom Hartmut Vallen, Vallen Systeme GmbH, Icking, Germany Michael C. Forde, University of Edinburgh, United Kingdom Els Verstrynge, University of Leuven, Belgium Guiyun Tian, University of Newcastle, United Kingdom Reuben Robert, Heriot-Watt University, United Kingdom Oh-Yang Kwon, Inha University, Korea Peter Tscheliesnig, TÜV Austria Services GmbH, Austria Antolino Gallego Molina, University of Granada, Spain Victor Shemyakin, Diapac, Russia Jean-Claude Lenain, Euro Physical Acoustics, France Athanasios Anastasopoulos, Envirocoustics, Greece Pedro Feres Filho, Physical Acoustics South America, Brazil Maochen Ge, Missouri University of Science and Technology, United States of America Janez Grum, University of Ljubljana, Slovenia Steven F. Wayne, University of Memphis, United States of America Jason Weiss, Purdue University, United States of America Alan A. Barhorst, Texas Tech University, United States of America Vaclav Svoboda, PREDITEST, Czech Park Jongwoon, Korea University of Technology & Education, Korea Didem Ozevin, University of Illinois, United States of America Gordon Schneider, Acoustic Technology Group, Inc., United States of America Jonathan Awerbuch, Drexel University, United States of America A. Tomoda, Kumamoto University, Japan Alexander Sorger, Chemnitz University of Technology, Germany Vladimir Genis, Drexel University, United States of America Jan Plowiec, Warsaw University of Technology, Poland Shifeng Liu, Soundwel Technology Company, LTD., China Zhen Huo, Wuhan Boiler Pressure Vessel Inspection Institute, China Guang Dai, Northeast Petroleum University, China Weihe Guan, Hefei General Machinery Research Institute, China Zhejun Liu, Aerospace Research Institute of Materials and Processing Technology, China

x

Committees

Yanting Xu, Zhejiang Provincial Special Equipment Inspection Institution, China Wei Li, Northeast Petroleum University, China Guanghai Li, China Special Equipment Inspection and Research Institute, China Jun Jiang, Nanjing Boiler & Pressure Vessel Supervision and Inspection Institute, China Zhanwen Wu, China Special Equipment Inspection and Research Institute, China

Contents

Part I

Instrumentation

Discussion on Elastic Wave Sources for AE Sensor Calibration at Low Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Longbiao He, Min Wang, Jian Kang, and Ping Yang

3

Proposal for an Absolute AE Sensor Calibration Setup . . . . . . . . . . . . . Hartmut Vallen

9

Difference Between FBG and PZT Acoustic Emission Sensor . . . . . . . . . Dong Xia, Peng Wei, and Chenggui Li

25

Research on Fiber Bragg Grating Acoustic Emission Sensor . . . . . . . . . Yang Yu, Guoliang Wang, and Bo Liu

37

The Optimization of Magneto Acoustic Emission Testing Device . . . . . . Yongna Shen, Gongtian Shen, and Wenjun Zhang

49

Power Over Ethernet Daisy Chained Acoustic Emission System for Structure Health Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Lowenhar and J. Dong

57

UNISCOPE: Instrument Integrating NDT Methods . . . . . . . . . . . . . . . . S. Elizarov, V. Bardakov, A. Shimanskiy, A. Alyakritskiy, D. Terentyev, V. Barat, A. Gogin, and V. Koltsov

65

Fiber Bragg Grating Acoustic Emission Demodulation System . . . . . . . . Yang Yu, Xiangyin Bu, Bo Liu, and Ping Yang

75

Part II

Signal Processing and Analysis

Application of Modal Acoustic Emission Technique for Recognition of Corrosion Severity on a Thin Plate . . . . . . . . . . . . . . Weigang Zhang, Jie Geng, and Yanting Xu

87

xi

xii

Contents

Research on the Identification of Crack Status Through the Axle Acoustic Emission Signal Based on Local Mean Decomposition and Grey Correlation Analysis . . . . . . . . . . . . . . . . . . . . 101 Lin Li, Wang Huawei, and Zhou Yong Acoustic Emission Characteristics Based on Energy Mode of IMFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Aijun Gu, Linsong Sun, Jindong Liang, and Wenqin Han Two-Dimensional Source Location of Acoustic Emission by Means of AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 H. Asaue, T. Shiotani, and K. Hashimoto Part III

Material Characteristics

Acoustic Emission RA-Value and Granite Fracture Modes Under Dynamic and Static Loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Xiling Liu, Zhou Liu, Xibing Li, and Jiahui Cui Damage Detection in Glass Fiber-Reinforced Plastics Using Ultrasonic Full-Waveform Comparison . . . . . . . . . . . . . . . . . . . . 155 Qiang Wang, Thomas Schumacher, and Ali Hafiz Fatigue Damage Evaluation of 2.25Cr-1Mo-0.25V Steel Using Acoustic Emission Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Mengyu Chai, Jinghai Xiang, Zongqi Zhao, Zaoxiao Zhang, and Quan Duan Acoustic Emission Behavior of TC4 Titanium Alloy Manufactured by Electron-Beam Free-Form Fabrication During Tensile Deformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Zhanwen Wu, Gongtian Shen, Yongna Shen, Junjiao Zhang, and Ran Liu An Entropy Approach for Characterization and Assessment of Fatigue Damage Accumulation in Q235 Steel Based on Acoustic Emission Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Zhonghui Jia, Jianyu Li, and Gang Qi Tensile Deformation Damage and Clustering Analysis of Acoustic Emission Signals in Three-Dimensional Woven Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 W. Zhou, Y. N. Zhang, and W. Z. Zhao The Study of Mechanical Behavior of Alloy Structural Steel Based on Dynamic Acoustic Emission Signal . . . . . . . . . . . . . . . . . . . . . 205 Xiaoli Li, Xinbo Chen, and Jinli Sun

Contents

xiii

Acoustic Emission Characteristic of Ceramic Matrix Composite Under Static Loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Yong Gao, Denghong Xiao, Liang Jin, Bo Jiang, Naitian Li, Quanhong Ye, Xiaohong Zhou, Zongkai Tong, and Fanchao Meng Part IV

Structure

Evaluation of Damage in RC Bridge Decks Reinforced with Steel Plates by AE Tomography . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Yiming Feng, Tomoki Shiotani, Yoshikazu Kobayashi, Takahiro Nishida, Hisafumi Asaue, Katsufumi Hashimoto, and Shigeru Kayano Damage Quantification Using an Improved b-Value for Concrete Slabs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 T. Shiotani, C. Granier, and K. Hashimoto Defect Diagnosis of Low-Speed Heavy-Duty Bearings Using Acoustic Emission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Guanghai Li, Yang Jiao, and Zhanwen Wu Investigation on Acoustic Emission Characteristics of Steel Structure of Amusement Device . . . . . . . . . . . . . . . . . . . . . . . . . 259 Junjiao Zhang, Gongtian Shen, Zhanwen Wu, Yilin Yuan, and Ran Liu Case Studies on Tank Bottom In-Service Acoustic Emission Testing and Its Verifications . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Yewei Kang, Zhenghong Guo, Yi Zhang, and Huatian Xu Corrosion Degree Evaluation and Leakage Judgment of Vertical Storage Tanks by AE Test . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Yanting Xu, Yadong Wang, and Weigang Zhang Acoustic Emission Testing and Evaluation of Ethylene Horizontal Tank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Yadong Wang, Zhi Xiang, Yanting Xu, Jiele Xu, and Zhongteng Lai Application and Research of Acoustic Emission in the Fatigue Test of Hoop-Wrapped Composite Cylinders . . . . . . . . . . . . . . . . . . . . . 297 Yaping Liu, Gongtian Shen, Yadi Yan, Yang Li, and Yong Zhang A Method for Small Leak Precise Location in Pressure Piping by Acoustic Emission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Ni Qin, Yongmei Hao, Xinming Yan, and Yunfei Yue Study of Acoustic Emission Attenuation Characteristics of the Steel Bifurcated Pipe in Hydropower Station . . . . . . . . . . . . . . . . 317 Weiping Wu, Shengjin Cheng, Dongfeng Li, Shulin Cao, and Bo Lü

xiv

Contents

Research on Pipeline Fault Diagnosis Technology Based on Automatic Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Xinying Wang, Xingshuai Song, Taiwang Yang, Huiran Zhang, and Haiqun Chen Acoustic Emission Testing of a Friction Stir Welding Aluminum Alloy Pressure Vessel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Jun Jiang, Cheng Ye, Zhongzheng Zhang, and Yongliang Yu Part V

Condition Monitoring and Diagnosis

Valve Leakage Analysis in Reciprocating Compressor by Using Acoustic Emission Technique . . . . . . . . . . . . . . . . . . . . . . . . . . 355 H. Y. Sim, R. Ramli, and A. Saifizul Determination of Characteristic Frequency Segments of Acoustic Emission Signal for Valve Leakage Detection in Reciprocating Compressor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 R. Ramli, H. Y. Sim, and A. Saifizul Acoustic Emission Fault Diagnosis of Rolling Bearing Based on Discrete Hidden Markov Model . . . . . . . . . . . . . . . . . . . . . . . 379 Fuping Guo, Shuqian Shen, Zhihong Duan, Zhiqing Fan, and Zhiwei Sun Part VI

Miscellaneous

Influencing Factors of Partial Discharge of Needle-Plate Based on Acoustic Emission Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Yu Zhang, Longbiao He, and Haijiang Zhu Application of Multistep Source Localization Method with Narrowing Velocity Interval in Mines . . . . . . . . . . . . . . . . . . . . . . . 399 Longjun Dong, Daoyuan Sun, Weiwei Shu, Xibing Li, and Jian Wang Statistical Precursor of Induced Seismicity Using Temporal and Spatial Characteristics of Seismic Sequence in Mines . . . . . . . . . . . 409 Longjun Dong, Daoyuan Sun, Weiwei Shu, Xibing Li, and Lingyun Zhang A Method for Leak Detection of Spacecraft in Orbit Based on Beam-Forming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Lei Qi, Lichen Sun, Donghui Meng, Yong Wang, Wei Sun, and Rongxin Yan Improved Ray Tracing Method Based on the Snell’s Law . . . . . . . . . . . 429 Qingchun Hu Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443

Part I

Instrumentation

Discussion on Elastic Wave Sources for AE Sensor Calibration at Low Frequency Longbiao He, Min Wang, Jian Kang, and Ping Yang

Abstract To obtain an ideal wide-band elastic wave source for acoustic emission sensor calibration at low frequency, different kinds of sources including resonant transducers and lead breaking were tried. The velocity spectrums measured by the laser vibrometer were compared, and the transducer with the resonant frequency at around 250 kHz was found to be suitable for the AE sensor calibration at less than 100 kHz, and the peak frequency could be found correctly without any spectrum modification for the elastic wave source. The traceability of pressure sensitivity (dB re 1 V/μbar) of AE sensors was also discussed according to the air-borne measurement. Keywords AE sensors · Calibration · Low frequency · Elastic wave source

1 Introduction Accurate calibration of the acoustic emission sensor is the premise of quantitative AE technology [1]. ISO and ASTM Standards mostly described the calibration methods from 100 kHz to 1 MHz with certain uncertainty [2–5]. Currently in partial discharge detection and other fields, AE sensors with resonant frequency at 20–80 kHz are always used. How to calibrate the sensors with high accuracy is a concerned problem. The primary standard for AE sensors calibration is to establish a system with reference transducer, such as a special capacitive transducer in ISO 12713. Now the reference transducer could use the laser interferometer instead of the capacitive transducer. Another problem is to find a suitable elastic wave source acting on the

L. He (*) · M. Wang · J. Kang · P. Yang Division of Mechanics and Acoustics, National Institute of Metrology, Beijing, People’s Republic of China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_1

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block. If the elastic wave source could be considered as a real wide-band one, which means that at least the velocity spectrum in the interested frequency range is flat, the relative frequency response of the DUT (device under test) sensor could be obtained easily even without any reference transducer [6].

2 Calibration System and Elastic Wave Selection The AE sensor calibration system was established at NIM according to the ISO standards, as shown in Fig. 1. This system contains elastic wave source, laser vibrometer, test block, analyzing and processing system, and so on. Both reciprocity and velocity measurement by laser vibrometer are included, and the results of reciprocity method and optical method have been proved to have good agreement in the system [7]. Burst signal could always get good signal noise ratio, and it is popular to use burst signal in hydrophone and ultrasound transducer calibration and measurement. For AE sensor calibration at low frequency below 100 kHz, if burst exciting waveform is used, the test block needs to be scaled up to several meters to avoid the influence of reflected waves. Therefore, pulsed elastic wave excitation is a proper way to get the frequency response of AE sensor. With spectrum analysis of the received pulse wave and the sensitivity at a reference frequency point, which could be obtained at relative high-frequency point, the frequency response of AE sensor in low frequency is obtained. In this paper, different elastic wave sources for low-frequency calibration were compared and discussed. We tried to find an easy way to get the frequency response of DUT sensor rapidly. It included different transducers excited by pulse, such as the wide-band transducer S9208 from PAC, resonant transducer V101 and V102 from

Fig. 1 AE sensor calibration system established at NIM

Discussion on Elastic Wave Sources for AE Sensor Calibration at Low Frequency

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OLYMPUS, breaking lead (2 mm in length and 0.5 mm in diameter), and breaking capillary glass tube. And an ultrasound transducer with the resonant frequency around 250 kHz was developed to quickly get the frequency response of AE sensor at low frequency below 100 kHz. The amplitude spectrum comparison was carried out with time selective window to get rid of the reflective wave from the edge of the test block. Another way to obtain the sensitivity of AE sensor in low frequency is using vibrator to get the reference velocity directly. But most of the commercially vibrators can only reach to around 50 kHz.

3 Results and Discussion From the figures in ISO 12713, we can see that for the breaking capillary glass tube, the amplitude at low frequency is nearly 20 dB less than that at several hundred kHz. How about other elastic wave sources in the velocity spectrum? Sound sources, including transducers excited by pulse and breaking lead, were tried, and the amplitude spectrum of the signal from the vibrometer comparison was carried out. Here Polytec OFV505 was used to measure the velocity at the surface of the block. The spectrum distribution as shown in Fig. 2 with normalized energy processing showed that breaking lead had more energy below 100 kHz comparing to the elastic wave excited by transducers V101 (resonant frequency at 500 kHz), V102 (resonant frequency at 500 kHz), and S9208 (wide-band transducer from PAC). Then the breaking lead was tried to measure the frequency response of one typical low-frequency sensor R3α from PAC. Test result of surface velocity frequency response by breaking lead showed that the maximum sensitivity of R3α is around -80

Amplitude spectrum/dB

-85 -90 -95 -100 -105 -110 -115

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-120 -125 -130 -135 -140

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Transducer 250kHz Breaking Lead

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Frequency/kHz 50 kHz, which did not meet with the nominal frequency range. The reason could be that the source of breaking lead doesn’t have enough energy in low frequency. When the customized ultrasound transducer with the resonant frequency at 250 kHz was used as the pulse source on the surface of the block, the maximum sensitivity of R3α was found to be around 30 kHz, as shown in Fig. 3. The results showed that elastic wave excited by the transducer with resonant frequency at 250 kHz was flatter in the low-frequency range and more suitable for the frequency response measurement of the AE sensors below 100 kHz. But for the response above 100 kHz comparing to the breaking lead, the curve obtained by the transducer was relatively lower, as shown in Fig. 3.

4 Discussion of Traceability for Pressure Sensitivity of AE Sensor The acoustic emission sensor manufactures, such as PAC and Vallen, use face-toface method to get the sensor’s frequency response. It was also suggested as a secondly calibration method [8]. With a wide-band transmitter, it is easy to get the frequency response in the unit of V/Pa or V/μbar with face-to-face method. The problem is how to determine the reference value at a specific frequency point. For a PZT element, it is assumed that the sensitivities in pressure mode (dB re 1 V/Pa) are the same in different mediums, while the sensitivities in velocity mode (dB re 1 V/m/ s) are different in different mediums. The pressure sensitivity could be obtained in air or in water by comparing with microphone or hydrophone [9]. Here the comparison with microphone measurement was carried out. As shown in Fig. 4, the sound source is a resonant transmitter at 40 kHz and the reference sensor is B&K 4939 microphone with the sensitivity around 4 mV/Pa. The

Discussion on Elastic Wave Sources for AE Sensor Calibration at Low Frequency

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Fig. 4 Test photo of the sound source and sensors

sound pressure level at the measurement point is 120.43 dB (re 20 μPa), which is 21.02 Pa. The output of the DUT sensor at 40 kHz is 1.6 mV with 40 dB gain. It is assumed that the same sound pressure acts on the diaphragm of microphone and the surface of AE sensor. It can be calculated that the pressure sensitivity of the DUT sensor is 0.000076 V/Pa, which is equal to 102.4 dB (re 1 V/μbar). If the sound source works at 100 kHz or other frequency, the sensitivity at the corresponding frequency point could also be measured. Here, with the face-to-face method, only the pressure sensitivity could be obtained, and the traceability could be solved in air or water by comparing with microphone or hydrophone.

5 Conclusions Elastic wave source selection is one of the key issues for AE sensor calibration at low frequency with pulse method. Sources, such as breaking lead and resonant transducer at above 500 kHz, may cause the frequency response peak moving to larger frequency if no correction for the spectrum is added, and they don’t suit for the calibration by direct spectrum analysis below 100 kHz, while the transducer with resonant frequency at round 250 kHz is better for low-frequency calibration. Faceto-face method is another way to get the frequency response, and the traceability could be solved in water or air by comparing with hydrophone or microphone in unit of V/Pa or V/μbar. Acknowledgment Natural Science Foundation of China (Nos. 51205378 and 51575502) and Special Fund for Scientific Research in Public Interest (201310010).

References 1. G.C. Mclaskey, S.D. Glaser, Acoustic emission sensor calibration for absolute source measurements. J. Nondestruct. Eval. 31(2), 157–168 (2012) 2. ASTM E1106, Standard Method for Primary Calibration of Acoustic Emission Sensors (ASTM Standard, 2002) 3. ISO 12713, Non-destructive Testing-Acoustic Emission Inspection-Primary Calibration of Transducers (ISO Standard, 1998)

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4. ISO 12714, Non-destructive Testing—Acoustic Emission Inspection—Secondary Calibration of Acoustic Emission Sensors (ISO Standard, 1999) 5. NDIS 2109, Methods for Absolute Calibration of Acoustic Emission Transducers by Reciprocity Technique (NDIS Standard, 2004) 6. P. Theobal, R. Pocklington, Velocity sensitivity calibration of AE sensors using the through wave method and laser interferometry, in 29th EWGAE, Austria, 2010, p. 54 7. Y. Wang, L. He, H. Zhu, P. Yang, Comparison of reciprocity and surface pulse methods for AE sensor calibration. Acta Metrol. Sin. 38(5), 621–625 (2017) 8. D. Xiao, Z. Zhang, H. Ren, Calibration principle for acoustic emission sensor sensitivity, in Advances in Acoustic Emission Technology, (Springer, New York, 2015), pp. 33–45 9. L. He, Y. Wang, H. Zhu, P. Yang, Discussion on the absolute calibration of piezoelectric acoustic emission sensors, in International Symposium on Precision Mechanical Measurements, China, 2016, p. 99030

Proposal for an Absolute AE Sensor Calibration Setup Hartmut Vallen

Abstract This paper is focused on the current lack of a practicable standard for AE sensor sensitivity calibration in absolute units of velocity or displacement, which requires the generation of a reproducible input motion at the AE sensor’s sensitive face. It points to the fact that the face-to-face (F2F) stimulation method of an AE sensor by a voltage-driven transmitter sensor has been introduced already in 1968 (by Harold Dunegan) and is still in use nowadays for relative sensor verification by sensor manufacturers and practitioners. This paper demonstrates that a modern scanning laser vibrometer (LVM) renders possible to scan the motion of the whole active face of a transmitter in a grid of, e.g., 1.7 mm by 1.7 mm and to deliver the frequency response function (FRF) for each scanned point in m/s per V reference voltage. This FRF output of the LVM can easily be converted to the “transmitting sensitivity” of the transmitter under test in dB referred to 1 m/s per V or 1 mm/s per V transmitter voltage. Furthermore, these measurements reveal whether how strong a radial resonance influences the motion at the transmitter’s face. This paper proposes to upgrade the relative F2F verification method to an absolute verification or calibration method by using a transmitter of calibrated transmitting sensitivity derived from LVM measurements. Key words Acoustic emission sensor · Calibration methods · Laser vibrometer · Laser interferometry

1 Status of AE Sensor Calibration AE sensor calibration has been an important issue in our field and was reviewed by Hill and Breckenridge [1, 2], and the F2F method was described [1]. Dunegan published no details of his method, which were recorded by Ono’s interview,

H. Vallen (*) Vallen Systeme GmbH, Icking, Germany e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_2

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reported in [3, 4]. Ono [4] made a systematic study of AE sensor calibration methods and updated the F2F method using laser interferometry, instead of hydrophone calibration used by Dunegan. Both transmitting and receiving sensitivities can be obtained in consistent manner. There are two problems in Ono’s methods. One is the use of only the center point for getting the transmitting sensitivity of a sensor. The other is effects of radial resonance of transmitters, producing spurious oscillations at low frequencies with some sensors of smaller sizes. Both require an improved laser instrumentation that can scan over the entire sensor surfaces. We already used a “scanning laser vibrometer” to overcome these challenges. The following lists all ASTM and international standards about absolute AE sensor calibration the author is aware of.

1.1

ASTM E-1106-2017, Standard Test Method for Primary Calibration of Acoustic Emission Sensors

Extract of original text: Scope: This test method covers the requirements for the absolute calibration of acoustic emission (AE) sensors. The calibration yields the frequency response of a transducer to waves, at a surface, of the type normally encountered in acoustic emission work. The transducer voltage response is determined at discrete frequency intervals of approximately 10 kHz up to 1 MHz. The input is a given well-established dynamic displacement normal to the mounting surface. The units of the calibration are output voltage per unit mechanical input (displacement, velocity, or acceleration).

This standard requires a transfer block of about 2 tons mass. Such a block exists at NIST, Boulder, CO, USA, but NIST decided not to offer AE sensor calibration service anymore.

1.2

ASTM E-1781-2013, Standard Practice for Secondary Calibration of Acoustic Emission Sensors

Extract of original text: Scope: This practice covers requirements for the secondary calibration of acoustic emission (AE) sensors. The secondary calibration yields the frequency response of a sensor to waves of the type normally encountered in acoustic emission work. The source producing the signal used for the calibration is mounted on the same surface of the test block as the sensor under testing (SUT). Rayleigh waves are dominant under these conditions; the calibration results represent primarily the sensor’s sensitivity to Rayleigh waves. The sensitivity of the sensor is determined for excitation within the range of 100 kHz to 1 MHz. Sensitivity values are usually determined at frequencies approximately 10 kHz apart. The units of the calibration are volts per unit of mechanical input (displacement, velocity, or acceleration).

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This standard needs a primarily calibrated reference sensor and a transfer block of at least 200 kg mass. Since primary calibration is no longer available, this standard cannot be applied for new AE sensor calibration facilities: If no primary, then no secondary calibration is available.

1.3

ISO TR13115-2011 Nondestructive Testing: Methods for Absolute Calibration of Acoustic Emission Transducers by the Reciprocity Technique

This standard has been critically examined by a recent publication [5] and, in the wording of its author, “invalidated.” It has reported that the reciprocity equation contains unknown values from the sensor under test and the reciprocity parameters had never been verified by any physical measurements. Besides, this standard also needs a heavy transfer block (about 200 kg mass minimum). This standard was based on the Japanese Society for Nondestructive Inspection standard, NDIS-2109 (2004), and has been only marginally revised since, so it is obsolete and should be substantially corrected or withdrawn.

1.4

ISO 12713:1998 Nondestructive Testing: Acoustic Emission Inspection—Primary Calibration of Transducers

This standard has the source from ASTM E1106. The revision status is of 1998. It is obsolete and should be withdrawn.

1.5

ISO 12714:1999 Nondestructive Testing: Acoustic Emission Inspection—Secondary Calibration of Acoustic Emission Sensors

This standard has the source from ASTM E1781. The revision status is of 1999. It is obsolete and should be withdrawn. Status: For establishing a new sensor calibration facility, no practicable and globally accepted standard exists!

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2 Sensor Verification in Face-to-Face Configuration Some sensor manufacturers as well as practitioners use the face-to-face (F2F) setup for relative sensor verification in order to make verification sheets available at the time of delivery of new sensors, to be compared with sheets from regular verification intervals or if doubts come up about the proper functionality of a sensor.

2.1

The Principle of F2F Sensor Verification

A PC-controlled function generator delivers a sinewave-sweep to the transmitter sensor, causing a well reproducible motion at the transmitter’s face. The directly coupled sensor under test (SUT) picks up the motion and converts it to voltage, amplified by a wide-band preamplifier (in Fig. 1 part of SUT). An AE system controls the function generator and digitizes and visualizes the SUT’s response, usually scaled in “dB referred to 1 V/μbar” input. Figure 2 shows the block diagram of the F2F setup. The principle of this F2F setup and the sensitivity reference to “V/μbar” originated from the hydrophone calibration of a standard transducer used in a F2F verification by Harold Dunegan in 1968. However, no systematic study of the F2F method was published until Burks’ and Hamstad’s paper in 2015 [6] and Ono’s paper in 2016 [4]. Ono clarified the correlation between reference scales of V/μbar, V/(m/s), and V/nm, so any one of them is workable. However, a reference to particle

Fig. 1 Face-to-face sensor verification setup. (a) (Left part) function generator and AE system, (b) SUT and transmitter with holding assembly

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Fig. 2 Block diagram for F2F setup

velocity or displacement is preferred for AE sensors as these are mounted to solid surfaces where motion is the appropriate input of the sensor.

2.2

Advantage of F2F Sensor Verification

• The F2F verification results in a curve showing the SUT response on a sine wave, measured in dB referred to 1 μVRMS, subtracted by a reference offset value for the scaling to dB referred to 1 V/μbar. The reference offset value has been shared to us by Harold Dunegan in 1997, for compatibility of our verification results with that of DECI Inc. • This verification does not need a transfer block; hence, it is easy to mobilize, can be performed everywhere, does not need a laboratory environment, and consumes less than a minute working time per SUT for use at quantities of SUTs. • This verification needs low-investment efforts, a function generator, a transmitter sensor, and proper software for the AE system. It delivers well reproducible charts, when used with same transmitter. • No aperture effect can happen, what avoids that a dip in response could be mistaken for an aperture effect. • By comparing results of a new verification run with one produced immediately after sensor delivery, one can easily judge whether the sensor behavior has changed or not.

2.3

Disadvantage of F2F Verification

Years ago, we discovered that using different transmitters of same model but different serial numbers results in frequency-dependent differences in the response curves. For one type of SUT, this can be solved by adding a correction curve vs frequency to the constant reference offset value (mentioned above). Unfortunately, the correction curve, obtained for one type of SUT, produces differences when used with another type of SUT. The following experiment demonstrates a non-plausible effect: Use transmitter units TA and TB of same model to obtain responses from two units of SUT of different model but same size of sensitive area, e.g., a 150 kHz and a 350 kHz resonant model. This gives four pairs of responses: SUT1A (transmitter

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A to SUT1), SUT2A, SUT1B, and SUT2B. We expected the difference SUT1A
SUT1B ¼ SUT2A
SUT2B. But this may fail by several dB. We suspect a nonhomogeneous motion distribution over the transmitter’s surface to be the reason for this behavior, since such nonhomogeneous effect was already mentioned in [6]. In the F2F setup, the transmitter produces a motion normal to the sensor surface, not the three-dimensional motion a surface wave or plate wave exhibits. Hence the sensitivity obtained from F2F measurement is assumed to be valid for waves that leave the test object normal to the surface. It may not correspond to the sensitivity to surface waves or plate waves. Reference [7] compares sensor sensitivities obtained from F2F measurement with those obtained from a bar wave setup, whereby users must be aware that the dimension (mainly thickness) of the plate or bar strongly influences the apparent sensitivity of the sensor. With this paper, we propose to turn the relative sensor verification toward an absolute sensor verification by using a calibrated transmitter of model Olympus V104 and by demonstrating that the transmitter can be calibrated to absolute units of m/sV by using a scanning laser vibrometer (LVM). Ono and coworkers demonstrated [3, 4] that also a laser interferometer can be used to obtain a calibrated transmitting sensitivity. We do not propose a method that obtains the apparent sensitivity of a sensor from surface or plate waves.

3 Proposal of a Method to Determine a Transmitter’s “Transmitting Sensitivity” in Units of m/sV 3.1

LVM Setup

The block diagram in Fig. 3b shows a function generator (FG) followed by an amplifier (Ampl.), the transmitter, and a 39:1 voltage divider to the reference input of the laser vibrometer (LVM) which scans the transmitter’s active face. The LVM device used for this work is limited to 2.5 MHz sampling rate and 1.25 MHz analogue bandwidth. We found the range up to 1 MHz works reliable; therefore, our graphs are limited to 1 MHz. The settings in detail: • FG, Amplitude 1 V (peak); sweep start, 10 kHz; sweep stop, 1.8775 MHz; sweep time, 8 ms. • Amplifier: Impedance in/out 50 Ω, gain 34 dB ¼ 50 at open output; 25 at 50 Ω terminated output (transmitter load at 10 kHz is equivalent to an open output). • Transmitter voltage (UT): 50 Vp nominal at 10 kHz, for decay over frequency see Fig. 4.

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Fig. 3 (a) Laser vibrometer scanning a point on the transmitter face, (b) block diagram, (c) grid of 213 scanned points on transmitter’s face on a 1.7 mm  1.7 mm grid

Fig. 4 Sinewave sweep from function generator at 1 Vp amplitude setting (at 50 Ω load) in time (μs) and frequency (kHz) domain, when transmitter model Olympus V104 is the sole load of the 50 Ω FG output impedance. 2 Vp measured at low frequency is due to transmitter’s high impedance. Voltage decrease with frequency is due to the transmitter’s capacitance (current increase with frequency). The transmitter voltage (UT) during LVM measurement starts with 48.85 V due to 50 amplifier and 1950 Ω load of 39:1 divider

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• Voltage divider 39:1 (R at UT: 1950 Ω) decreases UT below the input range of LVM reference input (10 V at 1 MΩ). The divider’s 1950 Ω load causes a decrease of UT by factor 1950/2000 from 50 Vp nominal to 48.75 Vp real. • LVM reference input: 1.25 Vp (48.75 V/39) ¼ UT (real)
31.82 dB. • LVM record length: 6.4 ms. • LVM spectral resolution: 6400 bins in 156.25 Hz interval for range 0–1 MHz. • LVM averaging: Each spectrum is the amplitude average (not a complex average) of 100 measurements. • Measurement duration: Approx. 20 min for 213 points.

3.2

Results from Laser Vibrometer

6400 bins output of LVM transferred to an EXCEL sheet demands a lot of PC resources and causes the PC to get extremely slow and unstable. Therefore, in a first step, the frequency resolution has been limited by averaging of 13 subsequent bins and an appropriate interpolation to steps of 2 kHz. Figure 5 shows the spectra delivered by the LVM, reduced to steps of 2 kHz, from 177 points in less than 12.3 mm center distance. Figure 5 also shows the scanned point positions in the x–z diagram. The vertical distance y was 300 mm. The spectra are shown as frequency response function (FRF), logarithmic scaled to mm/sV with reference to the voltage at the LVM reference input. This was transmitter voltage minus 31.82 dB. For getting the appropriate scaling for the transmitting sensitivity in dB re 1 mm/sV, the FRF values in Fig. 5 must be converted to dB and 31.82 dB subtracted (T in dB re 1 m/sV is T in dB re 1 mm/sV
60 dB).

Fig. 5 177 spectra from 177 scanned points on the face of the transmitter in less than 12.3 mm center distance

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Fig. 6 Average spectra over different center distances reference input of LVM for these curves was the function generator and not the transmitter voltage; therefore, the curves appear more flat than those in Fig. 5

Figures 6 and 7 show (scaled to m/sV) the average spectra for different center distances below 1.8 mm (red), 3.8 mm (light blue), 5.4 mm (green), etc. from 10 to 1000 kHz and 10 to 100 kHz, respectively. Obviously, the ripples of the curves increase toward the center of the transmitter’s active face. Above 100 kHz, the curves match well. Below 100 kHz, differences between the curves increase. The red curve is worst case; it represents the central point plus four neighbors. It should be mentioned that for Figs. 6 and 7, the LVM reference was erroneously taken from function generator instead from 39:1 divider; therefore, the curves are more flat. The signal measured was not a sinewave sweep but noise. Figure 7 shows the behavior around the strong dip at 40 kHz in finer resolution, especially that the differences between red, blue, and green curves are not negligible around 80 and 60 kHz. The LVM provides animations of the motion at selectable frequencies. Figure 8a, b shows the top and bottom motion of the face at 147 kHz (randomly selected) and Fig. 8c, d at 40 kHz (at the dip). The dip is obviously caused by a strong radial resonance that keeps the motion at the center small, compared to the even motion in Fig. 8a, b. For the calibration of small aperture sensors and for low-frequency sensors, the transmitter’s center point might not be ideal for matching the SUT center point. Details for defining rules for optimum out-of-center positioning still have to be evaluated.

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Fig. 7 As Fig. 6 but frequency range up to 100 kHz

Fig. 8 (a, b) Even motion at 147 kHz (randomly selected), (c, d) at 40 kHz (dip frequency). Dip seems caused by a strong radial resonance

However, as already mentioned, the averaged (FRF) spectra from LVM can easily be converted to a calibrated transmitting sensitivity spectrum of the transmitter under test by converting FRF to dB and subtracting the reference divider ratio of 31.82 dB. There is no need to further consider the decrease of ¼ UT due to the 39:1 divider, since the LVM used the measured voltage to calculate the FRF.

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4 Determining the Receiving Sensitivity of an AE Sensor in V per m/s and in V per nm The equation for determining the receiving sensitivity of a SUT, derived from (3) in [4], is “UR ¼ UT + RV + TV”, which is easy to convert to: RV ¼ ðU R
U T Þ
T V

ð1Þ

with RV: Receiving sensitivity in dB re 1 V (peak)/(m/s) (in short dB m/sV) UR: Response spectrum of SUT in dB re 1 μV (RMS) (in short dB μV) UT: Voltage spectrum at transmitter when SUT response spectrum is measured (more details below) TV: Transmitting sensitivity of transmitter in dB re 1 m/sV (peak) or dB re 1 mm/ sV (peak)
60, derived from LVM measurement From R in velocity unit (RV), one can obtain R in displacement unit (RD) dB re 1 V (peak)/nm by adding to RV the dB equivalent of 2 * pi * f and subtracting 180 dB ([4], p. 25): RD ¼ RV þ 20 log10ð2 piðÞ  f ½HzÞ
180 ½dB

ð2Þ

Before a series of SUT measurements, UT0 (RMS) shall be measured once according to Fig. 9a, using FG amplitude setting “UT0,” usually 25 mV (RMS). Transmitter and preamplifier input shall be the only load of the FG. This setting shall cause the measurement chain to reach at least half scale for good signal-to-noise ratio (SNR) but no saturation. The SUT response UR shall be measured according to Fig. 9b using FG amplitude setting “UT,” usually 100 mV (RMS). If high sensitive SUT types drive the

Fig. 9 (a) Top: Setup to measure UT0. (b) Bottom: Setup to measure UR

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preamplifier into saturation, or low sensitivity sensors do not reach 50% of full-scale range, setting “UT” must be adjusted accordingly. UT for use in (1) is defined by measured UT0 plus the dB equivalent of settings “UT/UT0,” usually 12 dB. Since both, the UT0 and UR measurement, use same preamplifier, the frequency response of the preamplifier and measurement chain do not influence the difference UR
UT.

5 Examples of Response Curves and Receiving Sensitivity Curves of Three SUT Types Figure 10 shows the response UR of three different SUT types (one sensor each) on two transmitters of model V104. Legend examples: “Ua1” is the response of “SUT 1” (VS900, light red) on transmitter “a.” “Ub3” is the response of “SUT 3” (VS150, dark green) on transmitter “b.” Light colors are used for transmitter a and dark ones for transmitter b. Serial numbers of sensors and Ids of transmitters are shown on top of the graph. The differences in one color are caused by the different behavior of transmitter a and b. These differences shall be eliminated by subtraction of T-sensitivity, obtained by laser vibrometer, from the response. This is shown in Figs. 11 and 12. Figure 11 shows receiving sensitivities in velocity unit [dB re 1 V/(m/s)] of SUT type 1 (red, VS900), 2 (blue, VS375), and 3 (green, VS150) on stimulation by Ta: V104-55 CD   > > > Aij ¼ 2 ai t j  a j t i > > >   > > = Bij ¼ 2 bi t j  b j t i   > Cij ¼ 2 ci t j  c j t i > > > >   > > Dij ¼ t i t j t i  t j > > > >  2    2 2 2 2 2 ; Eij ¼ t j ai þ bi þ ci  t i a j þ b j þ c j

Fig. 1 AE source location using four sensors with arrival time

ð1Þ

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where, Vp (m/s) is a propagation velocity of P wave, t0 is arrival time in sensor R0, and tN is arrival time difference between sensor RN and sensor R0 [3]. Result of source location S(x, y, z) has an error, because Vp is decided under the homogeneous media, namely, a unique velocity in this formula. Especially when the sensors are arranged sparsely, this error becomes large. To solve the problem, deep learning is applied to locate AE source in AE technique. Mathematical model for estimating unknown parameters by given data, the parameter optimization by descending gradient method, multilayer neural network, and error function to determine the quality of model parameters is required to analyze by deep learning. To satisfy those demands, TensorFlow developed by google as AI framework is applied in this study. At first in the deep learning, model function g is set as follows: gð Z 1 ; Z 2 ; . . . ; Z n Þ ¼ W 1 X 1 þ W 2 X 2 þ    þ W m X m þ b0

ð2Þ

where, Z is location of AE source, X is arrival time, W is weigh matrix, and b0 is bias vector. Next, probability of occurrence P as softmax function is set by sigmoid function σ() as follows: P ¼ σ ð W 1 X 1 þ W 2 X 2 þ    þ W m X m þ b0 Þ

ð3Þ

To distinguish sensors between first hit and nothing hit, arrival times on sensor of first hit and nothing hit are input 1 μs as minimum sampling time and 0, respectively. Column index i of tK vector is set 1 in when AE generated in Zi, otherwise set 0. K is the number of data. W is estimated by known tK as training data. Column index i shown maximum value in tK assumes that AE is generated in Zi. For the number of data K, probability P is denoted as follows: P ¼ ðP1 ðX m Þ; P2 ðX m Þ; . . . ; PK ðX m ÞÞ

ð4Þ

where, probability Pn for data number n is shown as follows: Pn ¼

YK

0

fPk0 ðX n Þgtk n k0¼1

ð5Þ

Probability P for all data n is shown as follows: P¼

YK

YN n¼1

0

k0¼1

fPk0 ðX n Þgtk n

ð6Þ

W and b0 is estimated which maximize P. Error function E is decided as follows: E ¼ logP ¼ 

XN n¼1

XK

t 0 logPk0 ðX n Þ k 0 ¼1 k n

ð7Þ

It is synonymous to maximize P and minimize E. Adam Optimizer is selected as an optimizer in this study [4, 5].

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3 Numerical Test AI with three network layers and each six nodes was applied for AE source location. Each arrival time was simulated by 2D velocity distribution model as shown in Fig. 2. The model consists of 15 grids Zn (n ¼ 1–15), and sensors Xm (m ¼ 1–4) arrayed on four corners. AE is located on the grid in this model. Each cell Si (i ¼ 1–8) framed in by four grids is given velocity value. Then, velocity anisotropy is reproduced in the model. Velocity distribution model set for training data on AI is shown in Table 1, in which 150 samples as the training data were prepared with 3% random noise to simulate the errors. Velocity range is set from 3000 to 4000 m/s. A set of test data was prepared as shown in Table 2 to verify the accuracy of the result. The combination of training data and test data is as shown in Table 3. Figures 3 and 4 show charts of Tables 1 and 2, respectively. AE source location by means of AI is calculated by five cases of the combination in this test. Set 1 and Test 1 are made from same velocity model in Case 1. Since result of Case 1 shows 100%, it can be said that effect of noise has been removed. Test 2 and 3 are made by different

Fig. 2 2D velocity distribution model

Table 1 Pattern list of 2D velocity model for training data

Training data model

Set 1 Set 2 Set 3 Set 4 Set 5 Set 6 Set 7 Set 8 Set 9 Set 10

Velocity (m/s) S1 S2 4000 4000 4000 4000 4000 4000 4000 3000 3000 3200 3000 3000 3000 3000 4000 3000 3000 3100 4000 3200

S3 4000 4000 4000 3800 3600 4000 3000 4000 3200 3600

S4 4000 4000 3000 3600 380Ū 4000 4000 3000 3300 4000

S5 4000 4000 4000 3400 3000 4000 3000 4000 3400 4000

S6 4000 3000 4000 3000 3200 4000 3000 3000 3500 3600

S7 4000 4000 4000 4000 3600 3200 3000 4000 3600 4000

S8 4000 4000 4000 3800 3800 3200 3000 3000 3700 4000

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Table 2 Pattern list of 2D velocity model for test data

Test 1 Test 2 Test 3

Velocity (m/s) S1 S2 4000 4000 4000 3000 4000 3000

S3 4000 4000 3800

S4 4000 4000 3600

S5 4000 4000 3400

S6 4000 3000 3000

S7 4000 4000 4000

S8 4000 4000 3800

Table 3 Pattern list of 2D velocity model for test data

Case 1 2 3 4 5

Estimated result (Correct answer: [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] [1 2 3 4 5 6 10 8 9 10 11 12 13 14 15] [1 2 3 4 5 6 10 8 9 10 11 12 13 14 15] [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]

Percentage of correct answer by AI 100.0 93.3 93.3 100.0 100.0

Training data Set 1 Set 1–4 Set 1–4 Set 1–10 Set 1–10

Test data Test 1 Test 2 Test 3 Test 2 Test 3

Fig. 3 Charts of Set 1 to 10 of velocity model

Fig. 4 Charts of Test 1 to 3 of velocity model

velocity models from training data set. Case 2 and 3 are calculated using 60 of training data. AE source on Z7 cannot be estimated both Case 2 and 3 otherwise succeed to detect AE source location. Case 4 and 5 are calculated using all of training data and Test 2 and Test 3, respectively. As a result, AE source location accuracy shows 100%, which is different from Case 2 and 3. For this reason, AE source location by means of AI can provide accurate results when enough numbers of training data were employed.

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4 Source Location Test in Corroded Concrete Beam Specimen A beam specimen was corroded by mixing 10 kg/m3 of chloride and leaving for about 3 months after casting. At that age, an AE measurement to verify the proposed source location algorithm by means of AI was conducted. The outline of the beam specimen is shown in Fig. 5. Pencil lead break and tapping by φ5 mm hammer on 15 grids as shown in Fig. 5 were carried out on the upper surface of the beam specimen. Elastic wave data were acquired in four sensors on the bottom. 2D source locations in two methods are calculated by formula (1) as Vp was assumed 3000, 3600, and 4000 m/s. The average errors with 3000, 3600, and 4000 m/s between located source positions and tapping points are as shown in Table 4. Both shows best result when Vp is 3000 m/s as shown in Fig. 6. Source location accuracy can be presumed to become lower because cracks have occurred due to corrosion of rebar inside of this specimen and velocity distribution became

Fig. 5 The outline of the beam specimen

Table 4 Average errors with 3000, 3600, and 4000 m/s between located source positions and tapping points

Vp (m/s) 4000 3600 3000

Average error (m) Pencil lead brake 0.242 0.211 0.178

φ 5 mm hammer 0.215 0.204 0.180

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Fig. 6 Source locations by formula (1) with 3000 m/s of Vp

Table 5 Results of Source location by means of AI The number of nodes in layer1 6

The number of nodes in layer2 6

The number of nodes in layer3 –

Case 1

The number of layer 2

2

2

6

4

–

3

3

2

2

2

4

3

4

4

4

5

3

6

2

4

6

3

6

6

6

7

3

8

8

8

8

3

15

15

15

Estimated result (Correct answer: [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]) [4 1 3 4 5 3 7 8 9 10 14 12 13 14 12] [4 1 3 4 5 3 7 8 9 10 14 12 13 14 12] [2 2 6 4 5 6 1 8 9 10 8 10 12 12 6] [5 2 12 7 5 12 7 8 8 10 10 12 13 13 13] [4 1 3 4 5 3 7 8 9 10 14 12 13 14 12] [5 2 6 8 5 6 7 8 6 10 12 9 13 14 15] [3 3 11 6 5 7 11 8 8 10 12 12 13 15 13] [5 2 3 4 5 3 7 8 9 10 13 13 13 14 15]

Percentage of correct answer by AI 66.7 66.7 46.7 46.7 66.7 60.0 33.3 73.3

inhomogeneous. Both results of source location almost succeed in the point of 02, 04, 05, 07, 08, 09, and, 10. However, it is distinctive that results of source location in other points were not estimated on true points due to the difference of the ray path to deviate the tapping point between two tapping methods or the influence of the noise. Next, source location by means of AI is carried out using arrival time data by pencil lead break as training data and by φ5 mm of hammer tapping as test data. The source location was conducted by changing the number of layers in the neural network and the number of nodes in the layer. Results are shown in Table 5.

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All cases converged within one million iterations. Case 8 employing the largest number of layers and nodes shows the most accurate case of 73.3% in percentage of correct answer; however, Case 7 as secondly large numbers of layers and nodes are employed, the result shows low accuracy of 33.3%. Hence, there is no correlation between the source location accuracy and the number of layers and nodes. In addition, as source location in all cases could not estimate the points of 01 and 11 and could estimate the points of 05, 08, 10, and, 13, it is obvious that there is a case when source location is possible and impossible. This is because that the influence of noise could not be eliminated in the experimental data. Because a limited number of training data have been used in this study, accurate source location with AI could not be realized.

5 Conclusions In this study, as using deep learning belonging to AI technology, accurate source location technique for a real civil structure having an inhomogeneous velocity distribution is proposed. Through numerical and empirical verification, it is resulted that, numerical experiments considering noise showed high source location accuracy, and possible and impossible case of source location are clearly divided in the corroded beam specimen. Therefore, it is important to increase the number of training data by adding random noise to carry out accurate source location by means of AI. Acknowledgment The authors wish to express sincere thanks to Dr. Nishida for helping to measure the data in corroded concrete beam specimen.

References 1. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015) 2. G.E. Hinton, L. Deng, D. Yu, G.E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T.N. Sainath, B. Kingsbury, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012) 3. Y. Niwa, S. Kobayashi, M. Ohtsu, Studies of source location by acoustic emission. J. JSCE 276, 135–147 (1978). (in Japanese) 4. I. Arel, D.C. Rose, T.P. Karnowski, Deep machine learning – a new frontier in artificial intelligence research. IEEE Comput. Intell. Mag. 5, 13–18 (2010) 5. A. Mohamed, G.E. Dahl, G. Hinton, Acoustic modeling using deep belief networks. IEEE Trans. Audio Speech Lang. Process. 20, 14–22 (2012)

Part III

Material Characteristics

Acoustic Emission RA-Value and Granite Fracture Modes Under Dynamic and Static Loads Xiling Liu, Zhou Liu, Xibing Li, and Jiahui Cui

Abstract The RA-value characteristics and distribution versus rock acoustic emission average frequency, the peak frequency characteristics, and the evolution of rock fracture modes under dynamic and static loads were studied to understand the fracture modes of rock under dynamic and static loads. The Split-Hopkinson pressure bar system and MTS322 servo-controlled rock mechanical test system were used, respectively, to carry out impact-loading tests and uniaxial compression tests at different loading rates. The results indicate that the RA-value under impact loading is higher in the initial stage, decreases to below 1 ms v
1 through the failure process, and even the variation trend tends to horizontal lines with loading time, which demonstrates that the fracture modes are dominated by tensile failure. An opposite variation in RA-value under static loading results when the loading rate is lower, but the variation corresponds with the impact-loading tests when the loading rate is higher, which indicates that tensile fracture still dominates the failure process and the occurrence of shear failure, as loads peak when the loading rate is lower. The acoustic emission signals exhibit a higher peak frequency under impact loads than those under static loads. Furthermore, in impact-loading tests, the peak RA-value will increase gradually with an increase in strain rate. The RA-value can be used to classify the crack type and as a rock fragmentation evaluation index. In general, the peak frequency can be used to distinguish two typical signals under impact-loading tests; signals with a higher peak frequency ( fp > 100 kHz) can be generated by rock fracturing, whereas those with a lower peak frequency and a higher RA-value can be generated by elastic wave propagation. Keywords Rock acoustic emission · RA-value · Rock fracture modes · SplitHopkinson pressure bar · Peak frequency

X. Liu (*) · Z. Liu · X. Li · J. Cui Central South University, Changsha, China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_13

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1 Introduction Rocks are mostly polycrystalline and brittle; thus the nucleation and propagation of cracks that are generated by rock failure will emit energy outward as elastic waves when subjected to loading conditions. Such elastic waves that are derived from microscopic dislocations, twinned crystals, crystal interfaces, and the slip and separation of macroscopic mineral grains, joints, and other weak planes are referred to as acoustic emission (AE) activity [1–5]. For polycrystalline and anisotropic rocks, it is a general trend that an extensive understanding of the development and propagation of cracks requires a knowledge of their fracture modes to understand the microscopic failure mechanisms of rocks more easily based on their structural complexity. A very important aspect is the close relationship of the AE parameters to fracture modes (tensile fracture, shear fracture, and their coupling fracture) [6– 11]. One of the critical parameters is the RA-value, which is a calculated feature that is derived from the “rise time” divided by the “amplitude” in ms v
1 and which shows the reciprocal of the gradient in the AE signal waveforms [7, 8]. The RA-value is extremely sensitive to the fracture modes. Frequency parameters, such as the average frequency, which is the number of threshold crossings over the signal duration, are measured in kHz. Another important frequency feature is the peak frequency ( fp), which is defined as the point in the power spectrum at which the peak magnitude was observed [11]. Many scholars have presented research on concrete’s fracture modes based on the characteristics of the AE parameters and have shown that the AE signals in shear failure had a longer waveform and lower frequency than those in tensile failure [9, 10]. Shear failure dominated at the last failure stages, whereas initially, the tensile mode was mostly active [12–14]. Several studies have shown that shear cracks lead to AE signals with a higher RA-value and lower frequency characteristics than tensile cracks [8, 15]. However, a series of achievements has been made specifically for rocks. For example, Shiotani [7] considered that the variation gradient of the ascending part in the waveform increased, which indicates that cracks of the tensile type were generated predominantly, whereas smaller values correspond to those that occur by shear type. Yang [16] proved that shear failure was a major microscopic failure mechanism of rock in a triaxial compression test that is based on a moment tensor analysis. Cheon [17] proposed that the current damage level of the rock slope and fracture type can be evaluated by changing trends and variation ranges of the AE parameters. Bucheim [18] pointed out that the AE signals of rock samples were characterized by a long duration time and a wide frequency spectrum when subjected to shear failure, whereas the results contrasted in tensile failure. Wang [19] demonstrated that AE signals always exhibited a higher amplitude and lower frequency in shear mode compared with a lower amplitude and higher frequency in tensile mode in three-point bending and shear fracture tests. In previous work, tensile and shear failure phenomenon were distinguished by their AE behaviors. Limited related work exists on the failure mechanism and AE characteristics of rock under impact loads. Therefore, we investigated the RA-value

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characteristics and its distribution versus the average frequency of the rock AE, the peak frequency characteristics, and the evolution of rock fracture modes under dynamic and static loading tests.

2 Experimental Details 2.1

Sample Preparation

To compare the fracture modes of rock under different loading conditions, an igneous rock granite was selected for impact and uniaxial compression loading tests. Five rock samples were prepared for each loading test, and their basic data are listed in Table 1.

2.2

Experimental Setup

Many comparisons between dynamic and static rock properties exist in rock mechanics and rock engineering [20]. Because the loading rate matches the drilling and blasting, a 50-mm-diameter Split-Hopkinson pressure bar (SHPB), as recommended by the International Society for Rock Mechanics Commission on Rock Dynamics, was used for the impact-loading test as shown in Fig. 1. A PCI-2 system and one ultra-mini sensor-PICO with a 550-kHz resonant frequency were used to collect the AE signals. The AE signals as detected by the sensor were pre-amplified by 40 dB. The detection threshold and sampling rate were set at 45 dB and 40 Msps, respectively. A MTS322 servo-controlled rock mechanical test system was used to carry out the uniaxial compression tests. In the uniaxial compression tests, the displacement control mode was used, with a loading speed of Table 1 Basic data statistics of each specimen Loading conditions Impact-loading tests

Uniaxial compression tests

Sample code A1 A2 A3 A4 A5 B1 B2 B3 B4 B5

Diameter (mm) 47.92 47.83 47.72 47.81 47.85 48.02 47.78 47.78 47.79 47.74

Height (mm) 26.06 25.91 26.11 26.25 25.70 98.41 99.06 99.06 97.56 96.85

Density (g cm
3) 2.64 2.64 2.67 2.63 2.59 2.62 2.65 2.65 2.66 2.65

Wave velocity (m s
1) 4137.21 4178.67 4280.19 4303.15 4355.81 4432.88 4762.50 4762.50 4414.48 4656.25

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4

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Fig. 1 Schematic of Split-Hopkinson pressure bar system with acoustic emission (AE) testing device. (1) Gas tank, (2) pressure vessel, (3) control valve, (4) striker, (5) light beams, (6) input bar, (7) strain gauge, (8) specimen, (9) output bar, (10) absorption bar, (11) dash pot, (12) electronic counter, (13) bridge, (14) ultra-dynamic strain gauge, (15) transient wave memory, (16) data processing unit, (17) AE sensor, (18) AE signal collecting and processing unit

0.0025, 0.025, and 0.25 mm s
1, respectively. A PCI-2 system and the ultra-mini sensor-PICO with a 550-kHz resonant frequency were used for AE signal collecting. Four sensors were arranged on the cylindrical surface for measurements during uniaxial compression testing. AE signals that were detected by the sensor were pre-amplified by 40 dB. The detection threshold and sampling rate were set to 40 dB and 10 Msps, respectively.

3 Experimental Results and Discussion 3.1

Analysis of RA-Value and Frequency Characteristics

The distribution of RA-value versus time from the static- and impact-loading tests (Fig. 2) shows that the RA-value under impact loading is higher in the initial stage and decreases suddenly to less than 1 ms v
1 through all fracture levels, and the variation trend tends to a horizontal line with the fracture progress. During the static loading tests, the RA-value is less than 20 ms v
1 in the early stage and then exceeds 100 ms v
1 during the fracture strength at a lower loading rate. With an increase in loading rate, the signals with a higher RA-value approach those of the initial stage, namely, the variation of RA-value, agree with the impact loading. The reasons can be derived from the impact of the equipment on the rock samples, which expands horizontally before the crack initiation, and leads to an increase in amplitude and rise time of the AE signals. However, the increase in rise time exceeds the amplitude significantly. The above results indicate that the granite fracture mode changes from a shear- to a tensile-type fracture with the progress of the fracture, but the fracture modes are dominated by tensile failure under impact loading. In static loading tests, the opposite occurs when the loading rate is lower; whereas the result agrees with the impact loading, when the loading rate is higher. These results prove that the granite fracture modes are more inclined to tensile failure with an increase in loading rate. Unfortunately, the major fracture mode cannot be obtained under static loading in Fig. 2. Much research [9–12, 21] has shown that the RA and average frequency (AF)

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Fig. 2 Variation of RA-value with loading time under impact loading (left) and static loading (right) in granite. Numbers A1–A5 represent different strain rates of 115, 109, 103, 85, and 72 s
1, respectively, under impact loading. Numbers B1–B5 represent different loading rates of 0.0025, 0.025, 0.25, 2.5, and 25 mm s
1, respectively, under static loading

distribution of each sample could evaluate the type of fracture. AE signals that are emitted from rock have a higher RA-value and a lower average frequency in shear failure than those in tensile failure. Figure 3 shows the RA and AF distribution of each sample (B1–B5) under uniaxial compression tests. Because much less data were collected for sample B3, the RA and AF distribution analyses for sample B3 were not performed. The left figure shows the distribution of the original RA and AF values, whereas the right figure shows the data density map as processed in MATLAB, which is better for visualizing the distribution features. The red squares cover the high-density core data, and they tend to approach the vertical axis, which indicates that in the uniaxial compression test, the granite fracture mode is dominated by tensile failure. This result agrees well with the common understanding that 90% of fractures in rock material under uniaxial compression loads results from tensile failure [22]. From the above results, we conclude that the rock fracture modes are dominated by tensile failure under dynamic and static loads. The evolution of rock fracture modes varies for these two loading conditions. The fracture morphology also proves this point, as shown in Fig. 4. The macroscopic crack propagation direction deviates from the axial pressure direction at a lower loading rate. As the loading rate increases, the direction of macro-crack propagation is parallel to the axial pressure, that is, the macroscopic fracture morphology of granite changes gradually from shear to tensile failure. At a higher loading rate, the macroscopic fracture morphology is the same as that which occurs under impact loading, namely, axial cracks are formed. The abovementioned AE descriptor (RA-value) is one of the most powerful in discriminating fracture modes [17, 18]. However, the individual fracture modes result in differences in other AE parameters, such as amplitude and peak frequency ( fp). Figure 5 shows the average peak frequencies and amplitudes for the two loading tests. The AE signals exhibit a higher amplitude and peak frequency under impact-loading tests than those under uniaxial compression tests. In general, it

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Fig. 3 AF versus RA-value for uniaxial compression test of granite. Numbers B1–B5 represent different loading rates of 0.0025, 0.025, 0.25, 2.5, and 25 mm s
1, respectively, under static loading

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Fig. 4 Fracture morphology for static loading and impact-loading tests of granite Fig. 5 Peak frequency versus amplitude for different loading conditions (each symbol is the average of all AE events in each experiment)

is accepted that AE signals exhibit a higher frequency in tensile mode compared with a lower frequency in shear mode [9–12, 19]. Although the granite fracture modes are dominated by tensile failure under static loading, the above results prove the existence of shear failure under static loads, which leads to the entire peak frequency ( fp) in the uniaxial compression tests being smaller than in the impact-loading tests.

3.2

Analysis of Waveforms in Impact-Loading Tests

In the impact experiment, the sensor on the rock sample will receive a signal that is generated by elastic stress-wave propagation, which can be considered an inherent characteristic of the rock AE under an impact load. However, if we distinguish the signal from the elastic wave propagation and that generated by rock fracturing, it

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should be helpful to understand the fracture characteristics of rock under the impactloading conditions. In the AE signal parameters, frequency is a major parameter that represents the source characteristics of the elastic waves. According to the stresswave signals that are detected by the sensor that is glued on the elastic bar in this experiment, the peak frequencies of these stress-wave signals are less than 100 kHz, and the signals that are generated by the stress waves in the rock specimen should be similar in waveform to those recorded by the input elastic pressure bar of the SHPB system. Therefore, we divide the AE signals of rock under an impact load into two parts, namely, those that exceed 100 kHz and those below 100 kHz at peak frequency. Table 2 lists the corresponding RA-value for these two signals and shows that the RA-value of the signal below 100 kHz at a peak frequency varies significantly, whereas those of the signals that exceed 100 kHz at a peak frequency change only slightly. It can be inferred that signals that exceed 100 kHz derive mainly from rock fracturing, but additional analytical methods are required to verify this result, such as the correlation analysis of signals. The coefficient of correlation can be used as an indicator to evaluate the similarity of signals in digital signal processing. If x(n) and y(n) are two AE signals, then the coefficient of correlation can be defined as follows: L P

xðnÞyðnÞ

n¼1

ρxy ¼ ½

L P n¼1

x2 ðnÞ

L P

y2 ðnÞ1=2

n¼1

where L is the length of the signals. The formula above shows that |ρxy|  1. If two signals are correlated completely (the same signal), then |ρxy| ¼ 1. If two signals are independent, ρxy ¼ 0. The strength of the correlation also depends on a significantdifference t-test. Figure 6 shows the waveform of a typical signal that is recorded on an input elastic bar. Figures 7 and 8 show the waveforms of typical signals that are recorded on rock samples with a peak frequency ( fp) that is lower and higher than 100 kHz, respectively. The waveform in Fig. 7 is more similar than that in Fig. 6. The calculated correlation coefficients of the various typical waveforms in Table 3 also show their similarity. In Table 3, symbol E is used to represent the waveform that is collected on the input bar. Symbols H1 and H2 are used to represent the waveforms of the signals with a higher peak frequency. Symbols L1 and L2 are used to represent the waveforms of signals with a lower peak frequency and a higher RA-value. The results show that signals H1 and H2 exhibit no correlation with that of E because p > 0.05. These results indicate that signals with a higher peak frequency are generated by rock failure, whereas the comparison between the signals of L1, L2, and E is statistically significant because p < 0.05. Although the ρxy of L1 and L2 is less than 1, it is larger than that of H1 and H2, so the signals of L1 and L2 could be derived from the elastic pressure bar, combined with the similarity of the waveform

Sample code Peak frequency ( fp) (kHz) RA-value (ms v
1)

A1 fP < 100 0–219

fP > 100 0–14

A2 fP < 100 0–150 fP > 100 0–8

A3 fP < 100 0–120 fP > 100 0–6

A4 fP < 100 0–85

Table 2 RA-value for peak frequencies ( fp) below and above 100 kHz for each specimen impact-loading test fP > 100 0–10

A5 fP < 100 0–61

fP > 100 0–8

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Fig. 6 Typical waveform of a hit that is recorded on the elastic pressure bar

Fig. 7 Two typical waveforms of a hit from a rock sample with a peak frequency below 100 kHz and an RA-value that exceeds 20 ms v
1

Fig. 8 Two typical waveforms of a hit from a rock sample with a peak frequency that exceeds 100 kHz

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Table 3 Calculated correlation coefficient of above waveform Symbol E H1 H2 L1 L2

Parameter Pearson ρxy t-test p(two-tailed) Pearson ρxy t-test p(two-tailed) Pearson ρxy t-test p(two-tailed) Pearson ρxy t-test p(two-tailed) Pearson ρxy t-test p(two-tailed)

E 1 0.000 1.000 0.004 0.604
0.018* 0.023 0.061** 0.000

H1 0.000 1.000 1
0.153** 0.000 0.006 0.486
0.001 0.914

H2 0.004 0.604
0.153** 0.000 1
0.062** 0.000
0.091** 0.000

L1
0.018* 0.023
0.006 0.486
0.062** 0.000 1
0.051** 0.000

L2 0.061** 0.000
0.001 0.914
0.091** 0.000
0.051** 0.000 1

Remarks: *, significant correlation on 0.05 level (two-tailed); **, significant correlation on 0.01 level (two-tailed)

features. To analyze their correlation, a t-test was used as an evaluation index to distinguish the signals of the elastic wave propagation from the signals that are generated by rock fracture based on the existence of elastic waves under impactloading tests. From the above results, we can probably conclude that signals with a higher peak frequency ( fp > 100 kHz) can be thought of as AE signals that are generated by rock fracturing. Those with a lower peak frequency and higher RAvalue can be regarded as signals of elastic wave propagation. Therefore, if the signals of the elastic wave propagation are eliminated, the RA-value will be below 1 ms v
1 through the failure process. Even the variation trend tends to horizontal lines with loading time, as shown in Fig. 2, which indicates that tensile failure dominates the fracturing process when rock is subjected to impact loads.

4 Conclusions Under dynamic and static loads, the granite fracture modes are dominated by tensile failure. The evolution of granite fracture modes varies with different loading conditions: the granite fracture modes convert from tensile failure to shear failure under uniaxial compression tests when the loading rate is lower, whereas tensile failure tends to dominate the failure process. The RA-value under static loading is higher in the later period at a lower loading rate, which indicates the occurrence of shear failure as loads peak. If the signals of elastic wave propagation are eliminated, the RA-value will be lower at even less than 1 ms v
1, and the variation trend tends to horizontal lines throughout the fracture process, which indicates that tensile failure dominates the fracturing process when granite is subjected to impact loads. The AE signals always exhibit a higher peak frequency ( fp ¼ 250–400 kHz) under impact-loading tests compared with those under uniaxial compression tests. Although fp ¼ 100 kHz cannot be used as a boundary to distinguish signals that are

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generated by elastic stress-wave propagation from those generated by rock fracturing, the peak frequency can be used to distinguish two typical signals under impactloading tests. The signals with a higher peak frequency ( fp > 100 kHz) can be generated by rock fracturing, whereas those with a lower peak frequency and a higher RA-value can be generated by elastic wave propagation. Acknowledgments This work is supported by the National Key Research and Development Plan (Project No. 2016YFC0600706), the National Natural Science Foundation of Hunan Province, China (Grant No. 2016JJ3148), and the Open-End Fund for the Valuable and Precision Instruments of Central South University (CSUZC201701). The authors are extremely grateful for the financial support provided by these funds.

References 1. K. Mogi, Study of the elastic shocks caused by the fracture of heterogeneous materials and its relation to earthquake phenomena. Bull. Earthq. Res. Inst. 40, 125–173 (1962) 2. C.H. Scholz, The frequency-magnitude relation of microfracturing in rock and its relation to earthquakes. Bull. Seismol. Soc. Am. 58, 399–415 (1968) 3. C.H. Scholz, Experimental study of the fracturing process in brittle rock. J. Geophys. Res. Atmos. 73, 1447–1454 (1968) 4. C.H. Scholz, Microfracturing and inelastic deformation of rock in compression. J. Geophys. Res. 73, 1417–1432 (1968) 5. X.L. Liu, X.B. Li, Acoustic emission characteristics of rock under impact loading. J. Cent. South Univ. 22(9), 3571–3577 (2015) 6. S. Yuyama, Z.W. Li, Y. Ito, et al., Quantitative analysis of fracture process in RC column foundation by moment tensor analysis of acoustic emission. Constr. Build. Mater. 13(1–2), 87–97 (1999) 7. T. Shiotani, M. Ohtsu, K. Ikeda, Detection and evaluation of AE waves due to rock deformation. Constr. Build. Mater. 15(5–6), 235–246 (2001) 8. D. Soulioti, N.M. Barkoula, A. Paipetis, et al., Acoustic emission behavior of steel fibre reinforced concrete under bending. Constr. Build. Mater. 23(12), 3532–3536 (2009) 9. D.G. Aggelis, A.C. Mpalaskas, T.E. Matikas, Investigation of different fracture modes in cement-based materials by acoustic emission. Cem. Concr. Res. 48(2), 1–8 (2013) 10. D.G. Aggelis, A.C. Mpalaskas, D. Ntalakas, et al., Effect of wave distortion on acoustic emission characterization of cementitious materials. Constr. Build. Mater. 35, 183–190 (2012) 11. D.G. Aggelis, T.E. Matikas, Effect of plate wave dispersion on the acoustic emission parameters in metals. Comput. Struct. 98–99(5), 17–22 (2012) 12. D.G. Aggelis, Classification of cracking mode in concrete by acoustic emission parameters. Mech. Res. Commun. 38(3), 153–157 (2011) 13. C.U. Grosse, F. Finck, Quantitative evaluation of fracture processes in concrete using signalbased acoustic emission techniques. Cem. Concr. Compos. 28(4), 330–336 (2006) 14. K. Ohno, M. Ohtsu, Crack classification in concrete based on acoustic emission. Constr. Build. Mater. 24(12), 2339–2346 (2010) 15. Y. Lu, Z. Li, W.I. Liao, Damage monitoring of reinforced concrete frames under seismic loading using cement-based piezoelectric sensor. Mater. Struct. 44(7), 1273–1285 (2011) 16. S.Q. Yang, H.W. Jing, S.Y. Wang, Experimental investigation on the strength, deformability, failure behavior and acoustic emission locations of red sandstone under triaxial compression. Rock Mech. Rock Eng. 45(4), 583–606 (2012)

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17. D.S. Cheon, Y.B. Jung, E.S. Park, et al., Evaluation of damage level for rock slopes using acoustic emission technique with waveguides. Eng. Geol. 121(1), 75–88 (2011) 18. W.W. Bucheim, Geophysical Methods for the Study of Rock Pressure in Coal and Potash Salt Mining (International Strata Control Congress, Leipzig, 1958), p. 222 19. H.J. Wang, D.A. Liu, Z.D. Cui, et al., Investigation of the fracture modes of red sandstone using XFEM and acoustic emissions. Theor. Appl. Fract. Mech. 85, 283–293 (2016) 20. K.W. Xia, W. Yao, Dynamic rock tests using split Hopkinson (Kolsky) bar system – a review. J. Rock Mech. Geotech. Eng. 7(1), 27–59 (2015) 21. S. Rippengill, K. Worden, K.M. Holford, et al., Automatic classification of acoustic emission patterns. Strain 39(1), 31–41 (2010) 22. G. Manthei, Characterization of acoustic emission sources in a rock salt specimen under triaxial load. Bull. Seismol. Soc. Am. 95(5), 1674–1700 (2004)

Damage Detection in Glass FiberReinforced Plastics Using Ultrasonic Full-Waveform Comparison Qiang Wang, Thomas Schumacher, and Ali Hafiz

Abstract Glass fiber-reinforced plastics (GFRPs) are the most commonly used in making chemical tanks and high-pressurized vessels. The squared Pearson correlation coefficient (R2) was applied to detect the small damages of GFRP. In this paper, we propose and evaluate a new way to detect small defects in GFRP parts based on ultrasonic full-waveform comparison. The general concept is that, as opposed to conventional ultrasonic testing where only the first arrival is used to determine a time of flight (TOF), the entire recorded waveform, including the Coda portion, is used. Keywords Glass fiber-reinforced plastics · Ultrasonic wave · Full-waveform · Squared correlation coefficient

1 Introduction Today fiberglass is the dominant reinforcement fiber in composite construction. Glass fibers are primarily composed of silicon dioxide with some modifying agents [1]. E-glass (electrical glass) accounts for the largest production of glass fibers in industry due to its low cost despite its mechanical properties that are lower than other grades of glass fibers. It is specially used in making chemical processing tanks and pressure vessels [2]. These tanks and vessels are usually large in size. Pipe branch connections, manholes, etc. in the vessels are very common and are potential weak points. These openings contain discontinuities in the structure. The large size combining with the discontinuities limits the operating pressure of these vessels. Each tank and vessel is custom made and they are different. The purpose of Q. Wang (*) Department of Quality and Safety Engineering, China Jiliang University, Hangzhou, China e-mail: [email protected] T. Schumacher · A. Hafiz Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_14

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inspection and evaluation of glass fiber-reinforced plastics (GFRP) is to determine the current condition of the asset and to identify short- and long-term repairs that may be required. Unlike many other materials traditionally tested by NDT, the velocity of ultrasonic wave propagation in glass fiber-reinforced plastics cannot be predicted accurately. The glass fiber direction has effect on the velocity of GFRP specimen. Great differences in wave velocity can be expected for different structures, and some variations can also be expected in one and the same structure, particularly one of this fiber direction and fiber size. The highly anisotropic and inhomogeneous properties in the thickness direction cause a high degree of attenuation of acoustic waves. Some assess structural integrity of GFRP using acoustic emission (AE) testing [3]. It was proposed to investigate by acoustic emission the damage behavior of the glass/polyester filament wound composite material by using a simple longitudinal specimen cut from pipes. Lock-in thermography allows for ascertaining whether a structure has undergone any damage based on the phase images of GFRP specimens [4]. A terahertz time-domain spectroscopy (THz-TDS) imaging system was devised to detect hidden multi-delamination in a glass fiber-reinforced plastic (GFRP) composite laminates [5]. Terahertz (THz) reflection imaging is applied to characterize a woven glass fiber-reinforced composite laminate with a small region of forced delamination [6]. Ultrasonic velocity measurement is a well-established method to measure properties and estimate strength as well as detect and locate damage in a carbon fiber-reinforced polymer plate [7]. A complete ultrasonic monitoring of glass fiber-reinforced cement plates based on broadband signals (chirp) under bending tests was addressed. It is demonstrated the suitability of ultrasonic broadband signals for characterizing fiber-reinforced cementitious composites under bending stress [8]. Above methods have used in the in the experimental investigation of the glass fiber composite behavior and require specific equipments, which in most of the cases increase the cost and research time. The correlation coefficient is a measure for the strength of the linear relationship between two variables, and its use to compare ultrasonic signals from concrete was proposed in [9, 10]. The squared linear correlation coefficient, R2, was used to compare and interpret the recorded ultrasonic waveforms. Hafiz and Schumacher have used R2 to detect the stress variation change in concrete cylinder specimens [11]. This stress online monitoring systems can benefit from the use of correlation calculation. The recorded ultrasonic waveform can be divided into two portions: the coherent portion and the diffuse portion or Coda wave [12, 13]. The GFRP is similar to concrete materials, a kind of complex heterogeneous material. So we hope to use the Coda wave phase shift and R2 characterizes the small damage changes in GFRP. In this paper, the ultrasonic waveform comparison method was used to detect the damages in GFRP sample. Prior to the test, a series of drilling holes was made in sample. At the same time, a 100 kHz Sine-type ultrasonic pulse was transmitted from one transducer and recorded by a second one. Each recorded waveform was compared with the reference waveform (prior to drilling) using the squared-correlation coefficient, R2, which can take a value of 1 (waveforms are identical) to 0 (no correlation). The results obtained are useful in describing the damage size (drill geometries) for GFRP material.

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2 Experimental Setup We focus on the ultrasonic wave behavior in the GFRP and hope to discuss the simple method to detect defects inside the GFRP. To relate the small defects in GFRP changes to variations of the ultrasonic waveform, the squared correlation coefficient R2 was used, which is also referred to as the coefficient of determination [11]. A perfect coherence would result in a coherence value of 1 over the whole frequency range. We use two sensors to transmit normal wave (100 kHz, sine wave pulse) and compare the received waveforms variation with different holes size in GFRP specimen. The specimen dimension for the test was 10.2
8.05
1.98 mm, which was cut from a mother plate. The sample consists of Φ ¼ 10 μm glass fiber filament diameter; these fibers are winded to Φ ¼ 670 μm fiber thread. In the GFRP specimen, nine holes (1/16, 5/64, 3/32, 7/64, 1/8, 9/64, 5/32, 0.199, 0.25 in.) were drilled in chronological order. The possibility of holes size variation detection based on R2 was explored. During the drilling process of GFRP specimen, an ultrasonic pulse was transmitted and recorded by two identical ultrasonic normal-wave transducers (Model: Panametrics V103). These transducers were installed on the surface of the specimen, as shown in Fig. 1. One of the transducers (transmitter) was used to send a 100 kHz Sine-type pulse excited directly by an arbitrary waveform generator (Manufacturer: BK Precision). The other transducer (receiver) captured the ultrasonic waveform passing through the specimen in Fig. 2. The output voltage of the waveform generator of 10 V. A preamplifier (Model: Olympus 5660B) was used for amplification. Data acquisition was performed with a high-speed transient recorder (Model: Elsys TraNET 204s) at a sampling frequency of 10 MHz. The typical waveform of transmitted pulse was presented in Fig. 3. In Fig. 4, both transmitted pulses are the same waveforms, while the received waveforms are complex. They were recorded at two different situations (non-defect, drilling hole). Non-defect signal is red color, and drilling hole received signal is blue color. No difference in the waveform can be observed between the non-defect and

Fig. 1 The setup of full-waveform comparison experiment

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$UELWUDU\ :DYHIRUP JHQHUDWRU

+LJKVSHHG GDWDUHFRUGHU

WUDQVPLWWHU

5HFHLYHU







P

P

Fig. 2 Illustration of GFRP sample test process (transmitted pulse and received pulse)

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Fig. 3 Transmitted pulse in the time and frequency domain. (a) Transmitted pulse waveform. (b) Transmitted signal spectral

drilling hole situations. The recorded waveforms were shown its complexity compared with the original transmitted pulses (as shown in Fig. 4). The recorded waveforms consist both of coherent and diffuse portion. In Fig. 4a, the coherent portion does not show obvious amplitude and phase shift. Though recorded waveforms are similar from each other, the diffuse portions exhibit subtle differences in phase in Fig. 4b. The diffusion (or Coda) portion is sensitive to small changes in the drilling hole size. In Fig. 5, it shows samples of reference waveform and two typical drilling holes damages (hole size 1.6 and 3.2 mm) waveforms, which were recorded at the same levels. It is clear recorded waveforms do not differ from each other in time and frequency domain.

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Fig. 4 The typical received waveforms of reference signal and drill hole received signal. (a) Sample window from the coherent portion. (b) Sample window from the diffusion portion

(a) Non-defect reference signal and its spectral

(b) drilling hole damage 1.6mm received signal and its spectral

(c) drilling hole damage 3.2mm received signal and its spectral

Fig. 5 The reference signal and two drill holes signal waveforms and its spectrum. (a) Non-defect reference signal and its spectral. (b) Drilling hole damage 1.6 mm received signal and its spectral. (c) Drilling hole damage 3.2 mm received signal and its spectral

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3 Data Processing 3.1

Correlation Coefficient

A number of indicators can be used to define the similarity between two waveforms [5]. The correlation coefficient can be used to measure the linear dependence between two signals. The most common correlation coefficient, generated by the Pearson product-moment correlation, may be used to measure the linear relationship between two variables. ρ¼

covðy0 ; yi Þ σ y0 σ yi

ð1Þ

where y0, is the reference waveform recorded no damage in panel, the yi is a waveform recorded at the ith size holes, and σ yiσ y0 are the standard deviations of two signals, respectively. A correlation coefficient of 1 or 1 indicates that the waveforms are perfectly identical. A correlation coefficient of 0 means waveforms are no correlation. In this study, the squared correlation coefficient was used, which is also referred to as the coefficient of determination, R2 : R2 ¼ ρ2 (Fig. 6). The R2 was introduced to compare the recorded waveforms under different drilling holes damages. The relationship between R2 and damaging hole diameters

1

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D are approximately linear, with R2 decreasing from 1 to 0.92. The drilling hole diameter increases from 1.6 to 6.35 mm. In the linear regime, R2 is mostly affected by differences in the diffuse portion of the recorded waveforms. The relationship is controlled by the drilling hole sizes in this test. Actually, the damages are often caused by glass fiber cracks or bond cracks that develop inside the specimen. A strong correlation was found where the R2 value decreases with increasing hole diameter D, even the drilling hole diameter increment is 0.4 mm.

4 Results and Discussion The laboratory experiment has proved the high accuracy of this method to detect the defects variation in GFRP. Thought the ultrasonic wave propagation in inhomogeneous material GFRP is complex, it is shown the obvious linear relationship between the R2 and hole sizes. Comparison of ultrasonic full waveforms is sensitive to the very small changes of drilling hole in the GFRP materials. The transmitted pulse frequency is 100 kHz sine. Based on time of flight, ultrasonic wave velocity in glass fiber specimen in different direction was calculated, v1 ¼ 5368 m/s (length) and v2 ¼ 3049 m/s (thickness) (Table 1). Rw1 and Rw2 are wavelength-to-hole size ration. The damaging process in experiment includes nine different drilling holes damages. The 1# hole size is 0.0016 m. From 1# drilling hole to 7# hole, the diameter increment is 0.0004 m. The ultrasonic full-waveform detection system performance is characterized by wavelength-to-hole size ration. For example, drilling the 1# hole, the ultrasonic wave velocity is 5368 m/ s and wavelength λ1 is 0.05368 m. So the method detection damage precision Rw1 has improved about 33.5 times. In the second situation, when velocity is 3049 m/s and wavelength λ2 is 0.03049 m, the Rw2 is 21.8. It is clear that damage detection of GFRP based on ultrasonic waveform comparison can improve the resolution many times. For example, when the drilling hole diameter D is 6.35 mm and wavelength is 53.68 mm, the Rw1 is 8.5. The defect size can be significantly smaller than the wavelength of the transmitted pulse. This experiment has demonstrated the ability of the squared correlation coefficient R2 to monitor the GFRP damages. Table 1 The time wavelength-to-hole size ration under two situations Hole label 1# 2# 3# 4# 5# 6# 7# 8# 9#

Hole size (m) 4/64 in. ¼ 0.0016 5/64 in. ¼ 0.002 6/64 in. ¼ 0.0024 7/64 in. ¼ 0.0028 8/64 in. ¼ 0.0032 9/64 in. ¼ 0.0036 10/64 in. ¼ 0.0040 0.199 in. ¼ 0.005 0.25 in. ¼ 0.00635

v1 (m/s) 5368 m/s

λ1 (m) 0.05368

Rw1 33.5 26.84 22.4 19.2 16.8 15 13.5 10.8 8.5

v2 (m/s) 3049 m/s

λ2 (m) 0.03049

Rw2 21.8 15.2 12.7 10.9 9.5 8.5 7.6 6.1 4.8

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5 Conclusion Ultrasonic wave propagation paths are complex due to the heterogeneity and nonlinearity of the GFRP material. In contrast, the diffuse (or Coda) portion is very sensitive to small changes in the damage of GFRP panel. One normal-wave transducer was the pulse transmitter and the other one is the receiver. A strong correlation was found where the R2 value decreases with hole diameter increment. A very clear linear relation exists between the R2 and drilling holes’ size. The described method is a valuable tool for detecting the damage of GFRP composite. The defect does not have to lie in the direct wave travel path. We just installed the two sensors on the random position of the GFRP panel. Furthermore, the defect size can be significantly smaller than the wavelength of the transmitted pulse. So this method is easy to be used in monitoring the loading GFRP vessel and finding probably damaging area; then other tools such as ultrasonic phased array or infrared imaging are applied to inspect defect sizes. Acknowledgment The authors acknowledge the support of the National Key Research and Development Program of China under grant 2017YFF0209704.

References 1. G.Z. Voyiadjis, P.I. Kattan, Damage of fiber-reinforced composite materials with micromechanical characterization. Int. J. Solids Struct. 30, 2757–2778 (1993) 2. R.H. Bossi, V. Giurgiutiu, Nondestructive testing of damage in aerospace composites, in Polymer Composites in the Aerospace Industry, (Woodhead Publishing, Cambridge, UK, 2015), pp. 413–448 3. H. Boussetta, M. Beyaoui, A. Laksimi, L. Walha, M. Haddar, Study of the filament wound glass/polyester composite damage behavior by acoustic emission data unsupervised learning. Appl. Acoust. 127, 175–183 (2017) 4. C. Meola, S. Boccardi, G. Carlomagno, Infrared Thermography in the Evaluation of Aerospace Composite Materials (Woodhead Publishing, Cambridge, UK, 2017) 5. C.-H. Ryu, S.-H. Park, D.-H. Kim, K.-Y. Jhang, H.-S. Kim, Nondestructive evaluation of hidden multi-delamination in a glass-fiber-reinforced plastic composite using terahertz spectroscopy. Compos. Struct. 156, 338–347 (2016) 6. J. Dong, A. Locquet, D.S. Citrin, Enhanced terahertz imaging of small forced delamination in woven glass fibre-reinforced composites with wavelet de-noising. J. Infrared Millim. Terahertz Waves 37(3), 1–13 (2016) 7. R. Livings, V. Dayal, B. Dan, Coda wave interferometry for the measurement of thermally induced ultrasonic velocity variations in CFRP laminates. Am. Inst. Phys. Conf. Ser. 1706(1), 5719–5731 (2016) 8. V. Genovés, J. Gosálbez, A. Carrión, R. Miralles, J. Payá, Ultrasonic broadband signals monitoring of glass-fiber reinforced cement (GRC) bending tests. Cem. Concr. Compos. 80, 55–63 (2017) 9. E. Niederleithinger, J. Wolf, F. Mielentz, H. Wiggenhauser, S. Pirskawetz, Embedded ultrasonic transducers for active and passive concrete monitoring. Sensors 15(5), 9756–9772 (2015)

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10. C.U. Grosse, F. Finck, J.H. Kurz, H.W. Reinhardt, Improvements of AE technique using wavelet algorithms, coherence functions and automatic data analysis. Constr. Build. Mater. 18(3), 203–213 (2004) 11. Ali Hafiz,Thomas Schumacher, “Monitoring of applied stress in concrete using ultrasonic fullwaveform comparison techniques. Nondestructive characterization and monitoring of advanced materials, aerospace, and civil infrastructure. Proc. SPIE 10169, 101692Z-1–101692Z-7 (2017) 12. S.C. Stählera, C. Sens-Schönfelder, E. Niederleithinger, Monitoring stress changes in a concrete bridge with coda wave interferometry. J. Acoust. Soc. Am. 129, 1945 (2011) 13. T. Planès, E. Larose, A review of ultrasonic Coda Wave Interferometry in concrete. Cem. Concr. Res. 53(11), 248–255 (2013)

Fatigue Damage Evaluation of 2.25Cr-1Mo0.25V Steel Using Acoustic Emission Entropy Mengyu Chai, Jinghai Xiang, Zongqi Zhao, Zaoxiao Zhang, and Quan Duan

Abstract Acoustic emission technique has become a promising and reliable tool for evaluating fatigue damage in engineering structures and materials. In this study, a new threshold-independent AE parameter, namely, entropy, was utilized for monitoring and evaluating the condition of fatigue damage in 2.25Cr-1Mo-0.25V steel during the fatigue crack growth process. The results showed that three fatigue damage stages were distinctly recognized by means of the variation of entropy with respect to fatigue loading time. Specifically, the first stage was related to fatigue crack initiation and small fatigue crack growth, and the second stage was associated with stable crack growth, whereas the final stage corresponded to unstable crack growth and final failure. Most importantly, the sudden rise of entropy could provide the warning sign at the critical damage point where the growing cracks propagated into the unstable growth phase. Additionally, fatigue damage mechanisms were correlated well with the generation of AE entropy by scanning electron microscope (SEM) analyses. Results from this work present a new strategy for evaluating fatigue crack growth in engineering materials and provide fundamental understanding on the fatigue mechanisms contributing to the generation of AE signals. Keywords Acoustic emission · Fatigue · Entropy · Crack growth · 2.25Cr1Mo0.25V

1 Introduction Evaluation of fatigue damage is essential for the integrity of engineering materials and components. Over the past few decades, acoustic emission technique (AET) has been extensively used as an important nondestructive testing tool for monitoring and evaluating the condition of fatigue damage of various materials in real time. M. Chai · J. Xiang · Z. Zhao · Z. Zhang · Q. Duan (*) School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_15

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Acoustic emissions (AE) are high-frequency transient stress waves generated by the rapid release of energy from localized sources within the material [1]. During fatigue crack initiation and growth, a major portion of the strain energies released by growing cracks or other microstructural deformations such as twining and crack closure are transmitted through the material as AE waveforms, which can be detected by the high-sensitivity AE sensor that is attached to the material surface. The severity of fatigue damage can, therefore, be evaluated based on the analyses of AE characteristics. So far, considerable research efforts have been devoted into the qualitative evaluation of the fatigue damage by means of AE parameters such as amplitude, count, energy, absolute energy, duration, and RA value (the value of rise time divided by amplitude) [1–9]. Specifically, count is a preferred option for characterizing fatigue crack growth as it is able to reflect the activity of AE source. For example, Han et al. [2], Jing et al. [3], Li et al. [4], and Chai et al. [5] adopted both count and cumulated count to characterize the fatigue crack growth in different metallic materials, and they divided the fatigue process into three damage stages involving crack initiation, stable crack growth, and rapid crack growth, according to the variation of count as a function of fatigue cycles. On the other hand, some researchers believed absolute energy is less related to the threshold than count, and therefore they recommended absolute energy as a top-priority parameter for evaluating fatigue crack growth and providing the warning sign at the critical point where the cracks begin to propagate rapidly. Besides, Aggelis et al. [8] calculated the RA value of each AE waveform recorded from fatigue crack growth in aluminum alloy and adopted both RA value and duration to assess the fatigue damage condition. Their findings indicated that there was an obvious transition of the cracking mechanism that dominated the fatigue crack growth behavior in the later period of fatigue. The cracking mode changed from tensile to shear, and such transition could be effectively recognized by a rapid rise of duration and RA value. The above outstanding investigations have shown AE technique is important and effective for evaluating the condition of fatigue damage of various materials and structures. According to the evolution of AE parameters, different failure stages corresponding to different severity of fatigue damage could be easily distinguished. Moreover, the critical damages like fatigue crack initiation or unstable crack growth could be obviously identified. This is especially significant for engineers and inspectors to accurately evaluate the damage condition of structure and to make an optimal decision to avoid serious fatigue failure. It is worth noting that the fatigue damage evaluations in above investigations are highly dependent on the de-noising approaches due to high mechanical noise disturbance condition during fatigue loading. The conventional de-noising methods include the parameter-based filter and the load-based filter. The parameter-based filter is a threshold determined by one common AE parameter or multiparameters. For instance, some researchers used an amplitude threshold filter or a multiparametric filter such as amplitude-duration filter to eliminate the noises and ensure the data quality [6–9]. However, notice that traditional AE parameters except for amplitude are highly related to the threshold (see Fig. 1) which is predetermined by

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Duration

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Fig. 1 Definitions of typical AE parameters

inspector or engineer before monitoring. Yet, if an improper threshold is determined by an inexperienced inspector under complicated monitoring conditions, the obtained AE parameters will probably be affected, and therefore significant uncertainties will be increased in the evaluation of fatigue damage. On the other hand, many investigators used a load-based filter to eliminate the external noises. They assume that AE signals generating near the peak load in every fatigue cycle are highly related to crack growth, whereas other signals are caused by noises [2, 3, 10– 12]. Thus, only these AE events are extracted and correlated to fatigue damage. It should be noted that this method needs the specific loading information, which may not be known in complex structures and loading conditions. Furthermore, only recording the AE events occurring near the peak fatigue load is not able to provide sufficient understanding of all AE sources contributing to damage development. Therefore, it is still a task of great challenge to accurately evaluate the fatigue damage by AET. In this work, a new threshold-independent parameter, AE entropy, which is proposed in our recent study [13], was utilized for monitoring and evaluation of fatigue crack growth in a high-strength low-alloy steel. The primary objective is to

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achieve an accurate evaluation of fatigue damage with the aid of AE entropy and to understand fatigue mechanisms contributing to the occurrence of entropy. This paper is organized as follows. Section 2 presents a brief introduction to AE entropy including its calculation procedures, advantages, and significances. Section 3 describes the experimental materials, devices, and procedures. The results and discussion of fatigue damage evaluation and SEM analyses are shown in Sect. 4, and the last section presents the conclusions.

2 An Introduction to AE Entropy As we all know, the information entropy, i.e., Shannon’s entropy, has the capability to quantitatively evaluate the uncertainty of a probability distribution. Given a random sequence {x1, x2,. . ., xn,}, Shannon’s entropy of this sequence can be obtained based on the following formula: H ¼ c

n X

pðxi Þ  logðpðxi ÞÞ

ð1Þ

i¼1

where H represents Shannon’s entropy, p(xi) represents the probability mass related to the value xi, and c is an arbitrary positive constant that dictates the units. In our previous work, we proposed a new parameter, i.e., AE entropy, to evaluate the microstructural change of material based on the definition of Shannon’s entropy. Assuming an AE waveform as a probability amplitude distribution, AE entropy can be defined as the uncertainty of such discrete distribution [13]. Figure 2 depicts the calculation procedures of AE entropy. Obviously, there are four steps. First, AE realtime monitoring during damage process of materials and structures is carried out, and all waveform information is measured simultaneously. Next, each waveform is converted to a matrix involving voltage values per microsecond to establish a random discrete sequence. Then, a histogram is established as a discrete probability distribution with a small bin width. Lastly, the AE entropy, i.e., Shannon’s entropy of each distribution, is calculated according to Eq. (1), and the variation of entropy as a function of arrival time is depicted. Evidently, in comparison with the calculation of traditional AE parameters such as duration, count, counts to peak, and RA value, the calculation of entropy theoretically makes use of all original AE waveform information contributing to damages. Furthermore, according to the calculation steps in Fig. 2, it can be known that AE entropy has no concern with the predetermined threshold. This is especially significant for reducing the effect of human causes and obtaining more accurate damage evaluation results. On the other hand, it is believed that a rise in AE entropy is mainly attributed to the increased AE intensity generated from enhanced damage variation of material. As a result, AE entropy is capable of characterizing the original microstructural deformations and accurately assessing damage condition during

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Fig. 2 Calculation procedures of AE entropy [13]

fatigue process. Thanks to these remarkable advantages, the effortless and accurate evaluation of fatigue damage can be achieved without knowing specific loading information and considering complex de-noising approaches.

3 Experimental Procedures The investigated material in this study is 2.25Cr-1Mo-0.25V steel, which is a typical high-strength low-alloy steel and has been extensively used in the fabrication of hydrogenation reactors and pipes. The chemical composition (in wt.%) of this material is 0.15 C, 0.16 N, 2.30 Cr, 0.05 Ni, 0.98 Mo, 0.54 Mn, 0.30 V, 0.10 Si, 0.05 Al, 0.009 P, 0.01 S, and balance Fe. Standard compact tension (CT) specimens were prepared to perform tension-tension fatigue crack growth tests according to ASTM standard E647 [14]. As shown in Fig. 3, the width and the thickness of the specimen are 55 mm and 12.7 mm, respectively. The sinusoidal cyclic load was applied to the specimen using a servo-hydraulic testing machine at room temperature. The peak load was 20 kN, the stress ratio is 0.1, and the loading frequency is 15 Hz. In order to accurately measure the change of crack size, direct current potential drop (DCPD) method was utilized with a constant current of 2 A. The change in crack size can be calculated according to the linear correlation between

170 Fig. 3 Illustration of the specimen and the FCG test (mm)

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potential drop and crack size. For more information about the DCPD method, see our previous work [15]. After the fatigue tests, a scanning electron microscope (SEM) was used to investigate the fracture surfaces and explore the fatigue mechanisms. The AE waveforms generated during fatigue crack growth test were continuously monitored by a SAMOS AEwin data acquisition system. A high-sensitivity sensor (R15a) and a preamplifier with a gain of 40 dB were jointly used to capture AE waves. The AE sensor was attached to the specimen surface with silicon grease. The operating frequency of this sensor is from 50 to 200 kHz. An amplitude threshold filter of 55 dB and a combined frequency filter of 100–400 kHz were adopted to eliminate extraneous noises. Typical AE parameters such as count, energy, and amplitude were obtained for characterizing fatigue crack growth behavior. Additionally, AE entropy of each waveform generated during fatigue tests was calculated to assess the condition of fatigue damage. The evaluation results of traditional parameters and entropy were compared and discussed.

4 Results and Discussion Figures 4 and 5 show the changes in count and amplitude with respect to loading time during fatigue test, respectively. The evolution of crack size with respect to time is also involved in the figures for comparison. Notably, the signals with low count ranged from 1 to 150 occurred through the whole fatigue test, while the signals with higher count appeared sporadically, especially at the beginning and near the end of the test. The evolution of amplitude showed a similar feature. The signals with low

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Fig. 4 The variation of AE count and crack size

Fig. 5 The variation of AE amplitude and crack size

count and amplitude could be attributed to the substantial cyclic plastic deformation at the notch or crack tip and stable fatigue crack growth. The signals with high count and amplitude at approximately 1000 s might be caused by crack initiation, while the high amplitudes and counts at the end of the test were highly associated with the rapid fatigue crack growth and final fracture. However, according to the above analyses, it is still difficult to accurately define the damage condition and distinguish different damage stages. The entropy of each AE waveform during FCG tests was calculated, and the variation of entropy with respect to loading time is shown in Fig. 6. It can be seen

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Fig. 6 The variation of AE entropy and crack size

that three fatigue damage stages were evidently discriminated. During the first stage, a continuous rise of entropy appeared owing to increased plastic deformation caused by high stress concentration ahead of the notch tip. At approximately 1200 s, AE entropy achieved its maximum value. Meanwhile, the measured crack size related to this point was 1.05 mm, where the arrow showed in Fig. 6. Many investigators have paid much attention to the fatigue crack initiation and small fatigue crack growth in metal materials by quantifying the crack size and studying the corresponding fatigue mechanisms. Most of them have reached a consensus that the size of physical short crack is larger than that of the plastic zone at the crack tip or size of several grains but less than 1–2 mm length [16–18]. In the present study, the crack size during the first stage was less than 1.05 mm, and therefore this stage was related to fatigue crack initiation and small fatigue crack growth. In the second stage, the evolution of entropy presented a relatively stable trend. Moreover, the entropies gradually increased with the crack size. It should be noted that AE entropy measures the disorder or the uncertainty of waveform generated from damage sources. As the plastic zone size ahead of the crack tip enlarged with fatigue loading time, the intensive energy released by plastic deformation within the plastic zone reasonably caused a continuous increase of disorder of waveforms, i.e., the value of entropy. As a result, the second stage could be attributed to stable fatigue crack growth, which occupied the dominant fatigue life during FCG process. In the last stage, a sharp rise of entropy emerged, and the values of entropy were much higher than those in the first two stages. At the same time, the crack propagated with a higher growth rate and the specimen fractured in a short loading time. Such behavior indicated the final stage was directly associated with unstable fatigue crack growth and final failure. The characteristic of entropy also demonstrated that the sharp increase of entropy could provide the warning sign at the critical damage point where fatigue crack

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Fig. 7 Normalized cumulated entropy, count, and energy

propagated into the unstable growth phase. Thus, the variation of entropy with respect to time or fatigue cycles is able to provide effective evidence and guideline for engineers to monitor and evaluate the condition of fatigue damage whether the material or structure fits the reliability purpose or possibly requires maintenance before the failure happens. The cumulated AE parameters such as cumulated entropy, count, and energy were normalized between 0 and 1. Figure 7 depicts the variations of cumulated entropy as well as the cumulated count and energy with respect to fatigue loading time. It is obvious that all the three curves exhibited a similar increasing trend. In stages 1 and 2, all the AE-cumulated parameters increased steadily because of substantial cyclic plastic deformation ahead of the notch or crack tip and stable fatigue crack growth. While a sharp increase of entropy caused by the unstable fatigue crack growth and final failure appeared in stage 3. The similarity in the three curves suggested that the cumulated entropy could also serve as an effective measure for evaluating the condition of fatigue damage like cumulated energy and count. Figure 8 displays the typical SEM figures of the fractured surface in different fatigue damage stages. The fracture surfaces revealed the transgranular cleavage fracture feature. Moreover, the fatigue striations became increasingly evident with the propagation of fatigue crack. Such obvious striation features suggested that the fatigue crack growth of 2.25Cr-1Mo-0.25V steel obeyed the Laird mechanism that is based on the growth of cyclic plastic deformation ahead of the crack tip. The growth of the cyclic plastic zone ahead of the notch or crack tip resulted in a successive increase of entropy, as shown in Fig. 6. In addition, when the crack propagated into stage 3, the roughness of the fracture surface greatly increased, as depicted in Fig. 8c, d. The increased roughness was commonly caused by the static fracture process, which appeared because of the enhanced fatigue damage ahead of the crack tip at

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Fig. 8 SEM fractographs of specimen for crack size of 1 mm (a), 5 mm (b), 16.5 mm (c) and 20 mm (d)

high stress intensity factor. This great microstructural change was consistent with the sudden rise of AE entropy in stage 3 and also contributed to the occurrence of signals with high amplitude and high count at the end of the fatigue crack growth test.

5 Conclusions In this investigation, a new threshold-independent AE parameter, i.e., entropy, was utilized for monitoring and evaluating the condition of fatigue damage in 2.25Cr1Mo-0.25V steel. The results have confirmed its remarkable capability to accurately evaluate the damage severity and to identify the critical damages without considering complex de-noising methods. Specifically, three stages associated with different fatigue damage mechanisms were successfully identified with the aid of entropy and cumulated entropy. Furthermore, the sudden rise of entropy could provide the warning sign at the critical damage point where fatigue cracks propagated into the unstable growth stage. Additionally, fatigue damage mechanisms were correlated well with the generation of AE entropy by SEM analyses. Our findings suggest that AE entropy is helpful for providing effective evidence and guideline for engineers and inspectors to accurately evaluate fatigue damage in engineering materials.

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Acknowledgment This study was supported by the National Basic Research Program of China (No. 2015CB057602).

References 1. ASTM E976-10, Standard guide for determining the reproducibility of acoustic emission sensor response, in ASTM Int., vol. 5, (ASTM, Philadelphia, 2010) 2. Z.Y. Han, H.Y. Luo, J.W. Cao, H.W. Wang, Acoustic emission during fatigue crack propagation in a micro-alloyed steel and welds. Mater. Sci. Eng. A 528, 7751–7756 (2011) 3. J. Jing, L. Dong, H. Wang, Y. Di, Y. Xu, Damage evolution and rupture prediction of 42CrMoA nitrided steel by acoustic emission and infrared thermography techniques. Nondestruct. Test Eval. 33, 376–392 (2018) 4. L. Li, Z. Zhang, G. Shen, Influence of grain size on fatigue crack propagation and acoustic emission features in commercial-purity zirconium. Mater. Sci. Eng. A 636, 35–42 (2015) 5. M.Y. Chai, Q. Duan, Z.X. Zhang, Acoustic emission study of fatigue crack propagation in Q345R. Chin. J. Eng. 37, 1588–1593 (2015) 6. J. Yu, P. Ziehl, F. Matta, A. Pollock, Acoustic emission detection of fatigue damage in cruciform welded joints. J. Constr. Steel Res. 86, 85–91 (2013) 7. N. Nemati, B. Metrovich, A. Nanni, Acoustic emission assessment of through-thickness fatigue crack growth in steel members. Adv. Struct. Eng. 18, 269–282 (2015) 8. D.G. Aggelis, E.Z. Kordatos, T.E. Matikas, Acoustic emission for fatigue damage characterization in metal plates. Mech. Res. Commun. 38, 106–110 (2011) 9. M. Chai, J. Zhang, Z. Zhang, Q. Duan, G. Cheng, Acoustic emission studies for characterization of fatigue crack growth in 316LN stainless steel and welds. Appl. Acoust. 126, 101–113 (2017) 10. T.M. Roberts, M. Talebzadeh, Fatigue life prediction based on crack propagation and acoustic emission count rates. J. Constr. Steel Res. 59, 679–694 (2003) 11. M. Rabiei, M. Modarres, Quantitative methods for structural health management using in situ acoustic emission monitoring. Int. J. Fatigue 49, 81–89 (2013) 12. B.A. Zárate, J.M. Caicedo, J. Yu, P. Ziehl, Deterministic and probabilistic fatigue prognosis of cracked specimens using acoustic emissions. J. Constr. Steel Res. 76, 68–74 (2012) 13. M. Chai, Z. Zhang, Q. Duan, A new qualitative acoustic emission parameter based on Shannon’s entropy for damage monitoring. Mech. Syst. Signal Process. 100, 617–629 (2018) 14. ASTM E647-15e1, “Standard test method for measurement of fatigue crack growth rates,” in ASTM Int., (ASTM, West Conshohocken, PA), 2015 15. M.Y. Chai, Q. Duan, X.L. Hou, Z.X. Zhang, L.C. Li, Fracture toughness evaluation of 316ln stainless steel and weld using acoustic emission technique. ISIJ Int. 56, 875–882 (2016) 16. H. Chang, E.H. Han, J.Q. Wang, W. Ke, Acoustic emission study of fatigue crack closure of physical short and long cracks for aluminum alloy LY12CZ. Int. J. Fatigue 31, 403–407 (2009) 17. S. Suresh, Fatigue of Materials (Cambridge University Press, Cambridge, UK, 1991) 18. M. Chai, Z. Zhang, Q. Duan, Y. Song, Assessment of fatigue crack growth in 316LN stainless steel based on acoustic emission entropy. Int. J. Fatigue 109, 145–156 (2018)

Acoustic Emission Behavior of TC4 Titanium Alloy Manufactured by ElectronBeam Free-Form Fabrication During Tensile Deformation Zhanwen Wu, Gongtian Shen, Yongna Shen, Junjiao Zhang, and Ran Liu

Abstract Acoustic emission (AE) testing was carried out in the TC4 titanium alloy manufactured by electron-beam free-form fabrication. The acoustic emission signal parameter distribution, localization characteristics, and frequency spectrum characteristic are analyzed in this chapter. The results indicated that there are many AE signals during the tensile deformation of the EBF3 TC4 titanium alloy material. The frequency of its AE signals shows it has a wide frequency band from 50 to 500 kHz, with a peak value of 150 kHz. The AE amplitudes are mainly distributed in 50–60 dB. The AE technique could be used as an effective means for the safety evaluation of the EBF3 TC4 titanium alloy material. Keywords Acoustic emission · TC4 titanium alloy · Electron-beam free-form fabrication (EBF3)

1 Purpose Electron beam free-form fabrication (EBF3) is a newly developed additive manufacturing process for manufacturing high-performance aerospace equipment components. In EBF3 process which is based on the computer-aided design data, the metal wire is melted by electron beam, fed into the molten pool, and deposited layer by layer to form a near-net-shape metal part. The EBF3 manufacturing process principle is shown in Fig. 1. The technology has advantages of fast speed and high internal quality, especially suitable for large complex metal structure as a whole, which has become an important means for modern aircraft with rapid development and low cost. EBF3 is mainly used in the manufacture of metal structural parts for aircraft. There are many studies on the acoustic emission characteristics of titanium alloys and their welded joints during tensile and fatigue, especially in the TC4 titanium Z. Wu · G. Shen (*) · Y. Shen · J. Zhang · R. Liu China Special Equipment Inspection and Research Institute, Beijing, China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_16

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Fig. 1 EBF3 manufacturing process principle

alloy pressure vessels [1]. But the EBF3 TC4 titanium alloy material because of its unique microstructure morphology is different from traditional casting and forging components; it is necessary to study EBF3 TC4 titanium alloy material’s acoustic emission signal during the damage process [2, 3]. In this paper, the acoustic emission behavior along the melting path of EBF3 TC4 titanium alloy material was studied during the tensile deformation.

2 Experiments 2.1

AE System

The AE measurements were realized with the AE system AMSY-6 (Vallen System, Icking, Germany) including sensors, preamplifiers, computer software, cables, etc. The broadband AE sensors are VS900-M and VS45-H and the resonant sensor is VS150-M. The bandwidths of the VS900-M, VS45-H, and VS150-M are 100–900 kHz, 45–450 kHz, and 100–450 kHz, respectively. The preamplifiers had a 40 dB gain with a 20–1000 kHz band-pass filter. The AE acquisition settings used for all specimens were the same: the threshold is 39 dB. The AE sensors were mounted on the test objects with screw clamps via the contact layer of a lubricant.

2.2

Tensile Tests

The AE signals generated during the tensile test could be used to provide information concerning crack. Thus, to obtain basic AE data of the EBF3 material, the tensile specimens were made for the tests, which were cut along the direction of melting path. The configuration of flat tensile specimen and the placement of AE sensors are shown in Fig. 2. The thickness or the tensile specimen is 2 mm. Three specimens

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Fig. 2 Tensile specimen of EBF3 and AE sensors. Sensors: 1# VS150-M, 2# VS900-M, 3# VS 45-H

were tested at ambient temperature, about 25  C. In order to decrease the machinery noises, such as the grinding of the clamps, the hinge pins contacted with specimens, and the backlashes in the clamps, the specimens were all subject to a preliminary loading in a universal testing machine under 1 kN prior to the testing. Then, the tensile test with simultaneous recording of the AEs was accomplished in the testing machine at a cross-head speed of 0.3 mm/min.

3 Experimental Results and Discussion 3.1

AE Parameter Distribution

The relationship diagram between AE parameters is usually used to show the features of AE sources. The diagrams for AE amplitude and the load over time are shown in Fig. 3a, b. From these history plots, the significant change of the AE events occurred at which time/load can be seen clearly. Figure 3c shows that there are two different acoustic emission signals during the tensile deformation process. Figure 3d shows that the AE amplitude of the AE events is mainly distributed from 50 to 60 dB. Figure 3 shows that there are many AE signals during the tensile deformation. At the beginning of the stretch, the acoustic emission signals are very active. The AE amplitude is about 80 dB. After entering the strengthening stage, there is only a small amount of acoustic emission signals, and the amplitude is below 60 dB. Before the fracture, a large amplitude acoustic emission signal appears.

3.2

AE Location Characteristics

Distributive AE events with location of the EBF3 specimen under the load are shown in Fig. 4. Figure 4 shows that there are AE location sources at fracture site. Also, the fracture site of EBF3 specimen can be localized by AE linear location method. The location velocity is about 500 m/s.

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Fig. 3 (a) Time vs. load and AE hits. (b) Time vs. load and AE amplitude. (c) Distribution of AE energy vs. duration. (d) Distribution of AE amplitude vs. hits

Fig. 4 (a) Distributive AE events vs. location. (b) Specimen fracture site map

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Typical AE Waveform and Frequency Spectrum

At fracture time, the waveforms and frequency spectral characteristics of the same AE signal could be received by the three AE sensors. Figure 5 shows the typical AE signal’s frequency spectrum of the same AE signal with different AE sensors. It’s a typical burst AE signal. The frequencies of AE signals have several peak values and are mainly distributed in the range from 50 to 500 kHz.

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Fig. 5 AE signal waveform spectrum of time domain and frequency domain (a) Sensor VS150-M. (b) Sensor VS900-M

4 Conclusion 1. During the tensile deformation of the EBF3 TC4 titanium alloy material, there are many AE signals. At the beginning of the stretch, the AE signals are very active. And it is quiet during the strengthening stage. 2. The AE amplitudes are mainly distributed in 50–60 dB. And the micro crack initiation and micro crack gathering correspond to higher energy, counts, and amplitude (less than 65–80 dB). 3. The frequency of its AE signals shows it has a wide frequency band from 50 to 500 kHz, with a maximum peak value of 150 kHz. 4. The AE events of the crack propagation could be located by using the AE linear location method. The AE technique could be used as an effective means for the safety evaluation of the EBF3 TC4 titanium alloy material. Acknowledgment This study was financially supported by the National Key R&D Program of China (No. 2017YFC0805700) and Chinese Aeronautics Science Foundation (No. 201554V3001).

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References 1. Z. Liu, L. Ge, et al., Acoustic emission estimate for small titanium alloy pressure vessels. Aerosp. Mater. Technol. 39(2), 21–24 (2009) 2. H. Suo, Microstructure and mechanical properties of Ti6A14V produced by Electron beam rapid manufacturing, Huazhong University of Science & Technology, 2014 3. K.M. Taminger, R.A. Hafley, Electron beam freeform fabrication (EBF3) for cost effective nearnet shape manufacturing, National Aeronautics and Space Administration, Langley Research Center, 2006

An Entropy Approach for Characterization and Assessment of Fatigue Damage Accumulation in Q235 Steel Based on Acoustic Emission Testing Zhonghui Jia, Jianyu Li, and Gang Qi

Abstract An understanding of damage accumulation in structural steel materials is of vital importance to the fatigue community in both academia and industry. A novel entropy-based approach is introduced to characterize and assess the fatigue damage accumulation in Q235 steel material. The presented technique is based on acoustic emission (AE) testing taking account into the valuable signal parameters extracted from the captured AE signals in the combination of static and dynamic cyclic loading procedures. Data from AE parameters are used as inputs for a multicomponent variate DA, which provides efficient statistical description of the fatigue damage state, enabling an assessment by the entropy method. The key aspects of this investigation include (1) the AE test with a new experimental paradigm fusing static and dynamic cyclic loading procedures, (2) the establishment of a multicomponent variate DA-based AE data, and (3) the assessment of fatigue damage accumulation using entropy-based method. These results open perspectives for predicting fatigue life and real-time damage recognition in Q235 steel material. Key words Q235 steel · Metal fatigue · Acoustic emission · Probability entropy · SEM

1 Introduction A consensus has emerged in science and engineering that the application of cyclic loading to a material will cause irreversible and cumulative damage, which is referred as fatigue damage [1]. Quantification of the fatigue damage is a basic and key research aspect of fatigue of materials and structures, especially for either design against fatigue of new structures or evaluation of remaining lifetime of existing Z. Jia (*) · J. Li Tianjin University of Science & Technology, Tianjin, China G. Qi University of Memphis, Memphis, TN, USA e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_17

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structures in service conditions. Two main categories have been come up to model the fatigue damage based on experiment. The first category is to develop a relationship between the length of fatigue crack and the number of accumulated loading cycles [2–4]. The other one is to measure permanent changes to the key mechanical properties with respect to the number of accumulated loading cycles such as yield strength, modulus of elasticity, tensile strength, and static toughness [5–7]. However, both of those two approaches are phenomenological, which are only limited to describe the last stage of process of fatigue, i.e., fatigue crack propagation and the final fracture. They may be failed to describe the first stage of fatigue damage, i.e., fatigue microcrack initiation and nucleation, which are considered to take up the clear majority of the whole fatigue life for some materials. In this work, a novel approach is proposed to characterize and assess the fatigue damage. Two features of this approach make it different from the phenomenological approach. The first one is that acoustic emission testing is employed to obtain the information of fatigue damage, which is well known to be able to acquire the information of microdamage. The other feature is that statistical approach is employed to analyze the signal of acoustic emission, which can effectively control the uncertainties contained in the microdamage process of materials and measurement process. As a result, we propose an entropy-based quantity to characterize the fatigue damage state and its evolution, which provide a synthesis assessment of the whole process of fatigue. Low carbon structural steel Q235 has been extensively used in the fabrication of machines and structures, such as compressors, steam and gas turbines, pumps, etc. Most engineering components made of Q235 steel undergoes cyclic loading in service. Thus, fatigue analysis is an important aspect in their design. So, without loss of generality for our approach, the Q235 steel was taken as the experimental material.

2 Fatigue Specimen and Experimental Procedure 2.1

Fatigue Specimen Preparation

Fatigue tests were conducted on specimens fabricated from Q235 steel plate, the chemical composition and typical mechanical properties of which are listed in Table 1. The test specimens were fabricated in hot rolled condition. Q235-B is a class of low carbon structural steel which is generally used without heat treatment in practical engineering applications. In this case, the specimen was ensured no heat treatment in its manufacturing process. All specimens were fabricated from the same Q235 base steel plate. Apparently, the section area and shape of the specimens will impact the specimen fatigue life. In this experiment, due to the shape and size of the AE sensor (Nano30, Physical Acoustic Co., PAC) used in static tensile tests, a rectangular cross-sectional specimen with a section area of 50 mm2 and width-to-thickness ratio of 2 has been chosen for more efficient tests.

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Table 1 Chemical composition and typical mechanical properties Grade Q235-B Grade

Q235-B

Chemical composition (%) C Mn 0.20 0.27 Mechanical property Tensile strength Yield strength (MPa) (MPa) 488

332

Si 0.023

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% elongation in 2 in. (50 mm) min

Impact test temperature ( C) þ20,0,20

22

P 0.016

Table 2 Average value of eight specimen’s static tensile test data σ b (MPa) 480

σ s (MPa) 340

Elongation (mm) 9.7635

The surface smoothness also exerts a greater impact on the specimen fatigue lifetime [8]. After the wire cutting process, the specimen was polished with emery paper (ANSI 80) artificially to remove its burr, angular, and oxide layer generated in the process of the high-temperature wire cutting.

2.2

Experimental Steps and Configuration

A group of preliminary static tensile tests were executed using universal testing machine (model MTS E45), and then dynamic fatigue tests were followed by a hydraulic fatigue testing machine (PLS-200, CIMACH). The ultimate tensile stress (UTS) σ b and the yield stress σ s of the specimens can be derived by the static tensile test. The loading level of fatigue test was decided by the stresses. Some typical static tensile test data are shown in Table 2. The fatigue loading has been ultimately set at
200 MPa with stress ratio R ¼ 1, mean stress σ m ¼ 0, and frequency f ¼ 20 Hz. This fatigue cycle stress level was lower than σ s, so the whole fatigue experiment was limited to non-plastic deformation in the view of macroscopic. The setups of AE data acquisition system (PAC, model Micro-II Digital AE System: 4-channels PCI-2) are preamplifier gain 40 dB; bandwidth of 100–400 kHz; PDT ¼ 300 μs; HDT ¼ 600 μs; and HLT ¼ 1000 μs. In this test, we used two AE sensors that were set directly on both sides of the test specimen from the center as shown in Fig. 1b, and the signal from the AE sensor was amplified to 35 dB. The resonant and the operating frequency ranges of the sensor are 140 and 125–175 kHz. As shown in Fig. 1a, one hierarchical experimental protocol has been used. Detailed procedures are the following:

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Fig. 1 Specimen and the hierarchical experimental test protocol. (a) Fatigue and static test procedure. (b) Specimen size and sensors location

• First-level procedure: first step is fatigue loading to 200,000 cycles and then second step static loading. • Second-level procedure: first step is fatigue loading to 300,000 cycles and then second step static loading. • Third step is fatigue loading to 400,000 cycles and then second step static loading. • Fourth step is fatigue loading to 500,000 cycles and then second step static loading. Additionally, a control group without fatigue damage was used.

3 Result and Discussion 3.1

Analysis of Static Tensile Test Data

If static tensile test data can approximately reveal the distinction or extent of the fatigue microdamage from different test conditions, the question would be more straightforward. Some previous researches about metal fatigue focused on the theory of residual strength (RS) [9, 10] have proven to be effective in the lifetime prediction [11, 12] and other aspects. But there is no quantitative description for the exact relationship between the two parameters.

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Fig. 2 Comparison of static test data of five test conditions

The results about the residual strength obtained from five different test conditions are compared with each other here, which is shown in Fig. 2. There is a certain regular distribution between load-time curves as shown in Fig. 2a which corresponds

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to the relationship of residual strength degradation. It seems that the yield area observed by these curves becomes more and more unobvious with increasing fatigue loading cycles, and it almost disappears under 500,000 loading cycles test condition. Furthermore, as shown in Fig. 2a, a little AE signals were acquired before 10 kN (200 MPa) which was not in line with the Kaiser effect. As shown in Fig. 2b, the residual strength corresponding to five test conditions shows a gradual decrease. Besides, there is no other obvious relation among these static tensile test data. Both indicate that the static data is not enough to provide a clear distinction for the process of fatigue damage accumulation in micro perspective.

3.2

Analysis Based on Probability Entropy

The characteristic parameters (time, amplitude, energy, count, etc.) of the AE signal from static test are combined to constitute “microdamage event” [13]. In order to reveal the interactions of randomly generated microdamage events (RGME) [14], a measurable multicomponent variate D [15] has been established. α1 ¼ f 1 ðC; t; A; E  Þ α2 ¼ f 2 ðC; t; A; E  Þ ⋮ αk ¼ f k ðC; t; A; E  Þ

ð1Þ

where t, A, and E denote the damage time, amplitude, and energy, respectively. C is the space coordinate system. Among these characteristic parameters, the amplitude is set as scale standard and equally divided into n subintervals, which means that n scales: [Amin, Amin þ (Amax  Amin)/n], [Amin þ (Amax  Amin)/n, Amin þ 2(Amax  Amin)/n], . . ., [Amin þ (n  1)(Amax  Amin)/n, Amax]. The stress levels are set as the observation index because the evolution of damage mainly depends on the loading, and the quantity m is determined by the observation index length. The multicomponent variate D matrix can be received by integrating the scale standard with aforementioned observation index: 9 8 Y1 > > > > > > > 0 Y2 > > > > > α11
 > > Yi > αm1 > > > > > > >⋮> ; : Ym

1    α1n ⋱ ⋮A    αmn

ð2Þ

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9 8 α1 j > > > > = < α2 j is the microdamage scale where column vector {X1, X2  Xj  Xn}; X j ¼ > >⋮> > ; : αmj 9 8 Y > > 1 > > > > > Y2 > > > > > = < ⋮ , Yi ¼ {αi1, αi2  αin} is the microdamage observation standard; row vector Yi > > > > > > > >⋮> > > > ; : Ym index. In D matrix, the element αij represents the quantity of AE event corresponding to observation value i and scale index j. It should be noted that the D matrix includes two forms: DA and DB. The difference between the two forms is that each observation index in DA is independent of each other, while the DB contains the cumulative result of the previous observation index. In order to investigate the microdamage evolution process of material in each stress stage, DA has been chosen as our analysis object. The probabilistic entropy s [16, 17] can be used to assess the uncertainty of microdamage evolution of material. A larger s can indicate greater uncertainty of the microevolution of random damage which contributes to more events of random damage (ERD) [18]; conversely, a smaller s can indicate lower uncertainty of the microevolution of random damage which contributes less ERD which means a more stable damage state. The probabilistic entropy formula is as follows [18]: s¼

n X

  p j ln 1=np j

ð3Þ

j¼1

where s is the probability entropy value, n is the number of scale subinterval, and Pj is the probability of ERD which falls into jth subinterval. All AE signals in the whole experiment had been processed by above analytical method. In order to give the entropy a wider growth space, we set the observation index at 60 MPa level. In this observation index length, there could accumulate more fatigue microdamage, and the difference in results can be correspondingly more obvious. The trajectory of damage states (TDS) [16] curves of these test conditions are as shown in Fig. 3. More attention would be paid on the preceding part of the TDS curves than the latter part or the entirety because the response of preexisting fatigue microdamage may be more obvious in the beginning of static tensile test corresponding to different test conditions. The TDS curves in Fig. 3a demonstrate an approximate parallel relationship in the initial stage of the entire curve. It’s assumed that with the increasing of fatigue cycles, the quantity of preexisting fatigue microdamage is increasing accordingly and the distribution of microdamage getting more uniform. This means a greater possibility of generating new damage or more serious damage which corresponds to a lager entropy value s. In the last stage of TDS, all the entropy curves tend to merge

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Fig. 3 TDS curves of five test conditions of Q235 steel

together, and this indicates that all the specimen from the five cases have a convergent damage state during the final static tensile phase because of the uniform property of the specimens. And the whole static tensile test can be described as an accumulation process of microscopic RGME which gradually evolves to a macroscopic fracture failure. But it’s especially worth noting that the entropy s of 500,000 fatigue cycles test condition decreases as shown in Fig. 3; more valuable microcosmic damage information would be shown by SEM observation.

3.3

Analysis of Scanning Electron Microscope Fractographs

In order to take microcosmic analysis about the fatigue microstructural damage, the fracture surfaces of the specimens were inspected by SEM (sigma 300, ZEISS), and images at 2500 magnification were chosen for the comparison of microstructural damage (voids and microcracks), as these are the indicators for ductile fracture, expected for low carbon steel. Since low carbon steel fails by ductile fracture, there is a large amount of plastic deformation on the fracture surfaces. Absent in Fig. 4 are brittle cleavage features, and instead, voids can be seen on all fracture surfaces ranging in size from submicron to ~2 μm, with the majority ranging in size between ~0.5 and 1 μm. The direct comparison of the five test conditions provides an opportunity to elucidate the effects of fatigue damage on the final specimen fracture behavior. In order to quantify the extent of damage present in each micrograph, we defined the range from 0.5 to 2 μm as all sizes void (all voids), and the sum of all voids is plotted in Fig. 5a. During ductile failure of material under tensile stress, ERD progress from the creation of small voids to their coalescence to form larger voids

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Fig. 4 SEM fracture surface micrographs detected from 0 to 500,000 fatigue cycles. (a) 0 cycle. (b) 500,000 cycles 233

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Number of voids

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(c) Smaller voids

Fig. 5 The account of voids computed from the micrograph of fracture surface of Q235 steel. (a) All voids. (b) Larger voids. (c) Smaller voids

that are believed to weaken the material [19]. For capturing the effect of fatigue on larger void (Fig. 5a) formation, a second inspection was made only looking for voids which size was larger than 2 μm (as shown in Fig. 5b). In addition, there was also a void count for smaller voids (Fig. 4b) which size has been defined as 0–0.5 μm (as shown in Fig. 5c). Because it cannot be denied that the material itself originally contains a certain amount of smaller voids, therefore it’s important to be pointed that “all voids” do not contain “smaller voids” in the counting result. Larger voids are seen on all fracture surfaces and result from microvoid coalescence, a process that continues under the influence of increasingly applied tensile strain and ultimately leads to ductile fracture [20]. The influence of fatigue on the formation of voids, and as such microstructural damage or weakening of the microstructure, can be seen in Fig. 5a where a level of 166 voids from the static test is seen to increase to 178 after 300,000 fatigue cycles. After 400,000 fatigue cycles, the void count reached 233 and remained 234 until 500,000 fatigue cycles. The increase in the number of voids created a higher probability of void coalescence and the formation of larger and potentially more weakening voids. Figure 5b shows that after 400,000 fatigue cycles, the number of larger voids increases from 16 to 21 voids and keeps the same quantity to the 500,000 fatigue cycles. As for the

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Fig. 6 Fatigue microcrack in fracture surfaces. (a) 400,000 cycles. (b) 500,000 cycles

smaller voids, the count in Fig. 5c shows no steady state. Thereby we think the fatigue cycle has no significant effects on the smaller voids count. It’s worth noting that during the observation procedure of fracture surfaces by SEM, there were some distinguishing features to be found. As shown in Fig. 6, two similar microcracks with obvious trend of elongation were displayed in the fracture surface of 400,000 and 500,000 fatigue cycles test condition, respectively. Meanwhile, the same appearance has not been found in other test conditions apparently. This is a conclusive evidence for proving the aforementioned “microvoid coalescence.” And the two test conditions were corresponding to the TDS curves in which entropy value s was increasing and decreasing, respectively. With the fatigue cycles increasing, microdamage inside the specimen accumulated to create a higher probability of new microdamage (larger size voids) generation. When cyclic loading achieved about 400,000 cycles, the quantity of voids got an approximate peak valve with a peak corresponding entropy curve which means a generation of microcracks. After 500,000 cycles, the void quantity approximately trended to be stable, and the TDS showed a downward trend representing the probability decreasing of new microdamage generation. It’s assumed that the quantity of new generated microcracks was relatively far less than the voids, and with the microcrack propagating until final fracture failure, the relative uniform distribution of microdamage evolved to the only and extreme effect: a major macroscopic fracture crack. During the whole experiment, fatigue microdamage (microvoid and microcrack) passed through a process from increasing to decreasing which corresponded to the change in the entropy value s.

4 Conclusion The fatigue examination of metal material, especially Q235 steel in this article, is limited to empirical theory and idealized modeling. Theoretical assumptions significantly contribute to discounting source data originated from the physical damage,

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and the simplification of the “deterministic” models also brings the bias of understanding physical nature. Comparing to the static test data, the distinction of fatigue microdamage in the five test conditions could get a more obvious indication by the description of probability entropy. And the entropy approach can complete a more reasonable microscopic description combining with the analysis of SEM fracture surfaces. All of these have shown that this methodology (the entropy approach) has a potentiality for fatigue microdamage detection and even fatigue life prediction of metal materials. Acknowledgment The authors would like to appreciate Dr. Steven F. Wayne’s kind help. Without the SEM analysis provided by him, this work couldn’t be completed so far. As a new graduate student, without my supervisor Prof. Li’s significant support, I couldn’t finish this work with only more than a half of a year’s AE learning. The same authors would like to thank Dr. Yingli Zhu (Tianjin University of Science and Technology) for his support and encouragement. The authors appreciate all the people who have offered help to this innovative work. Thanks again!

References 1. E.D. Norman, Mechanical Behavior of Materials (Pearson Education, London, 2013), pp. 282–305 2. L. Chen, L.X. Cai, Research on fatigue crack growth behavior of materials by considering the fatigue damage near the crack tip. Chin. J. Mech. Eng. 48(20), 54–59 (2012) 3. Y.J. Shang, Reliability analysis method for fatigue crack grown life of axle. J. Lanzhou Railway Univ. 4, 44–46 (2003) 4. X.L. Zheng, X. Xie, X.Z. Li, et al., Estimation model for steel wire crack propagation and its application in calculation of pre-corrosion fatigue life. Chin. Civil Eng. J. 50(3), 101–107 (2017) 5. S. Siddique, M. Awd, J. Tenkamp, et al., Development of a stochastic approach for fatigue life prediction of AlSi12 alloy processed by selective laser melting. Eng. Fail. Anal. 79, 34–50 (2017) 6. S.M. Du, S.R. Qiao, Damage evolution of 3D-C/Sic composite during fatigue based on variation of elastic modulus. J. Mech. Strength 34(4), 604–607 (2012) 7. M.G.F. Ana, R. Gabriel, F.D. José, et al., An investigation of bending fatigue crack propagation in structural steel by the measurement of indirect parameters. J. Braz. Soc. Mech. Sci. Eng. 37 (1), 305–312 (2015) 8. S. Blasón, C. Rodríguez, J. Belzunce, C. Suárez, Fatigue behaviour improvement on notched specimens of two different steels through deep rolling, a surface cold treatment. Theor. Appl. Fract. Mech. 92, 223–228 (2017). ISSN 0167-8442 9. T.P. Philippidis, T.T. Assimakopoulou, Using acoustic emission to assess Shear strength degradation in FRP composites due to constant and variable amplitude fatigue loading. Compos. Sci. Technol. 68, 840–847 (2008) 10. R.M.N. Fleury, D. Nowell, Evaluating the influence of residual stresses and surface damage on fatigue life of nickel superalloys. Int. J. Fatigue 105, 27–33 (2017). ISSN 0142-1123 11. S. Siddique, M. Awd, J. Tenkamp, F. Walther, Development of a stochastic approach for fatigue life prediction of AlSi12 alloy processed by selective laser melting. Eng. Fail. Anal. 79, 34–50 (2017). ISSN 1350-6307 12. X. Yuan, C. Li, An engineering high cycle fatigue strength prediction model for low plasticity burnished samples. Int. J. Fatigue 103, 318–326 (2017). ISSN 0142-1123

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13. L. Zhang, M. Fan, J. Li, Statistical analysis of events of random damage in assessing fracture process in paper sheets under tensile load, in Proceeding of the World Conference on Acoustic Emission-2013, vol. 158(3), (Springer, Shanghai, 2015), pp. 267–281 14. G. Qi, M. Fan, S.F. Wayne, Measurements of a multicomponent variate in assessing evolving damage states using a polymeric material. IEEE Trans. Instrum. Meas. 60(1), 206–213 (2011) 15. G. Qi, P. Jose, Z. Fan, 3-D AE visualization of bone-cement fatigue locations. J. Biomed. Mater. Res. 52(2), 256–260 (2000) 16. G. Qi, S.F. Wayne, G. Lewis, et al., Probabilistic characteristics of random damage events and their quantification in acrylic bone cement. J. Mater. Sci. Mater. Med. 21, 2915–2922 (2010) 17. G. Qi, A.A. Barhorst, On predicting the fracture behavior of CFR and GFR composites using wavelet-based AE techniques. Eng. Fracture Mech. 58(4), 363–385 (1997) 18. G. Qi, J. Li, M. Fan, Assessment of statistical responses of multi-scale damage events in an acrylic polymeric composite to the applied stress. Probabilistic Eng. Mech. 33(11), 103–115 (2013) 19. S. Wei, G. Wang, J. Yu, Y. Rong, Competitive failure analysis on tensile fracture of laserdeposited material for martensitic stainless steel. Mater. Des. 118, 1–10 (2017). ISSN 02641275 20. Y. Ogawa, D. Birenis, H. Matsunaga, A. Thøgersen, Ø. Prytz, O. Takakuwa, J. Yamabe, Multiscale observation of hydrogen-induced, localized plastic deformation in fatigue-crack propagation in a pure iron. Scr. Mater. 140, 13–17 (2017). ISSN.1359-6462

Tensile Deformation Damage and Clustering Analysis of Acoustic Emission Signals in Three-Dimensional Woven Composites W. Zhou, Y. N. Zhang, and W. Z. Zhao

Abstract To study the deformation and damage behaviors of three-dimensional (3D) woven composites, uniaxial tensile tests of the composites were conducted and real-time acoustic emission (AE) signals and speckle images were simultaneously obtained. A k-means clustering algorithm combined with principal component analysis (PCA) was used to analyze the AE signals, and the deformation fields on the surface of composite specimens were measured by the digital image correlation (DIC) method. It was found that the corresponding AE signals were divided into three typical clusters. On the basis of the frequency range of each type, the relationships between the resulting clusters and damage mechanisms were established. The results showed that different damage mechanisms in 3D woven composites are well determined by the frequency rather than the peak amplitude. The characteristics of AE signals such as the high frequency, the medium frequency, and most of the low frequency are associated with fiber damage, fiber/matrix debonding, and matrix cracking, respectively. The change in the displacement distribution can effectively enable visual inspection of damage accumulation in composites. Key words Three-dimensional woven composites · Clustering analysis · Acoustic emission · Digital image correlation

1 Introduction In recent years, three-dimensional (3D) woven composites have become attractive for applications because of their high rigidity, high strength to weight ratio, and good resistance to delamination in comparison with laminated composites [1, 2]. In addition, an integral reinforcing fiber network for 3D woven composites has been widely used in complex structures such as T-beams and circular tubes in spacecraft, ships, and automobiles [3]. However, the important issues for composites in practical applications

W. Zhou (*) · Y. N. Zhang · W. Z. Zhao Non-destructive Testing Laboratory, Hebei University, Baoding, China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_18

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are aging and development of internal damage in composite structures. Hence, to estimate the overall performance of composites and characterize the initiation and propagation of damage in composites, considerable attention is required. The appearance of different damage modes such as matrix cracking, fiber debonding, or fiber damage in composites is accompanied by the release of energy, which generates acoustic emission (AE) [4]. On the basis of this physical phenomenon, AE technology can be used for monitoring damage evolution and identifying damage mechanisms in composites [5, 6]. Because of the anisotropic nature of composites, the propagation velocity and wave attenuation of AE signals are complex, making it challenging to locate and quantify the damage [7]. Therefore, use of a reliable method to predict and identify damage from AE signals can provide guidance for users to decide what kinds of repairs are needed. Liu et al. [8] investigated AE response in two-dimensional (2D) carbon fiber/silicon carbide (SiC) matrix composites under fatigue loading and found that AE activity representing damage evolution in the composites was in quite good agreement with the modulus and hysteresis response of the composites. Shateri et al. [9] applied fuzzy c-means to analyze AE data on fiber-reinforced polymer composites subjected to tensile tests. They found that these isolated AE events could be classified into three clusters, and the ratio of AE signals in fiber damage to matrix cracking was an effective indicator for failure prediction in the composites. McCrory et al. [10] conducted delta-T mapping and used three AE classification techniques to locate and classify damage modes of carbon fiber panel buckling. It was found that the AE signals could be assigned to matrix cracking and delamination. In summary, these studies were focused on damage pattern identification and damage evolution monitoring of 2D composites. With regard to complex structures, the damage and evolution of 3D woven composites are not fully understood. Lomov et al. [11] monitored the tensile response of 3D woven carbon/epoxy composites by AE technology and concluded that damage characterization using AE relative energy can distinguish between different damage mechanisms. Furthermore, Li et al. [12, 13] investigated the relationship between AE signals originating from internal damage of composites under tensile loading and actual damage modes, and concluded that AE parameters such as the peak amplitude and peak frequency can be used for distinguishing different damage modes in the composites. As an optical, nondestructive technique, the digital image correlation (DIC) method can capture full-field deformation and strain information and can evaluate the fracture behaviors of composites. Wang et al. [14] adopted the DIC method to observe local damage evolution in a composite in tensile tests and concluded that a dominant localization band played a key role in macro-cracking and the ultimate fracture properties of the specimen under loading. More importantly, Cuadra et al. [15] estimated progressive damage accumulation in a glass fiber–reinforced polymer under fatigue loading and found that use of the AE and DIC methods could well describe hysteretic fatigue behaviors and could assess the residual life and reliability of the composites. Use of multitechnique analysis has individual merits, which have improved the reliability of damage assessment. The present paper describes use of complementary

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technology involving the AE and DIC methods to study damage progression and damage mechanisms in 3D woven composites under tensile tests. A k-means clustering algorithm and principal component analysis (PCA) were used to achieve damage mode recognition. Moreover, real-time deformation fields on the surface of the composite specimens were obtained to achieve visual damage inspection.

2 Materials and Tensile Tests In accordance with the tensile test reference standard ASTM D3039/D3039M-14, as-prepared 3D woven composites, with 12k carbon fiber for the transverse yarns and the weft and 3k carbon fiber for the binder warp, were cut into rectangles measuring 175 mm
25 mm
4.6 mm, using a water cutting machine, as shown in Fig. 1. Then, reinforced aluminum measuring 40 mm
25 mm was pasted on both ends of the specimens. Before the tests, an artificial speckle area measuring 30 mm
25 mm was sprayed in the middle of the specimens. To obtain data on the deformation and different damage mechanisms in the 3D woven composites, uniaxial tensile tests were conducted by a CMT5305 universal machine at a displacement rate of 1 mm/min. Meanwhile, AE signals originating from internal damage of the composite specimens and the speckle images on the surface of the specimens were collected by a DS2A instrument and a CMOS (MER-500-7UM-L, 2592
1944 pixels) camera, respectively. One RS-54A AE sensor with a frequency range of 100–900 kHz and a preamplifier with an output of 40 dB were used. The sensor was mounted on the back of the specimens, and silicone grease was used as the coupling agent. Meanwhile, a pencil lead break test was performed to verify good acoustic coupling between the specimens and the sensor. To eliminate electrical and mechanical noise, a suitable threshold was fixed at 10 mV (40 dB) by repeated tests. The sampling frequency was 3 MHz. The artificial speckle images on the surface of the specimens were recorded by a CMOS camera at different load levels during the tensile process.

Fig. 1 Schematic diagram of a three-dimensional woven composite specimen

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3 Results and Discussion 3.1

Mechanical Properties and Acoustic Emission Signals

The tensile load and AE relative energy versus time in three repeated 3D woven composite specimens are shown in Fig. 2. As shown in Fig. 2a–c, the similar time– load behaviors are correlated with the evolution of AE relative energy. In the initial loading stage, there is barely the appearance of AE signals and the time–load curve gradually increases. As the tensile load increases, an approximate linear relation between the time and load is obtained, more AE signals are generated, and the AE relative energy gradually increases. Because of the damage accumulation, the AE relative energy obviously increases at the final stage and the values are even over 10,000 mV•mS, which leads to the failure of the composite specimens. The failure loads of specimens S-1, S-2, and S-3 are 62.5, 59.5, and 61.7 kN, respectively. In addition, as the tensile load increases up to approximately 25% of the failure load, the AE relative energy increases remarkably. As a result, the activity of the AE signals can effectively be used for monitoring and quantification of progressive damage accumulation in the composites. The number and distributions of the AE peak frequency and amplitude of the three specimens are shown in Fig. 3. For specimens S-1, S-2, and S-3, similar distributions are obtained. As shown in Fig. 3a, fewer AE signals with a high peak

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frequency are generated and almost 85% of AE events are focused on the frequency range from 50 to 150 kHz. Similarly, the peak amplitude is mainly distributed in the range of 45–65 dB, as shown in Fig. 3b. Because of the reproducibility of the data set, one typical specimen was selected for an in-depth analysis, which is described in the next section.

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Multivariable Analysis of Acoustic Emission Signals

In comparison with the single AE characteristic parameter, multiparameter analysis of AE signals can better reflect the real AE response behaviors of composites. Because of the correlation of AE signals such as relative energy, peak frequency, duration time, counts, peak amplitude, rise time, and centroid frequency, reasonable selection of AE parameters before multiparameter analysis of AE signals can improve the quality of the classification. On the basis of previous studies, the amplitude, peak frequency, centroid frequency, and RA value (rise time divided by amplitude) are used as input vectors for the k-means clustering algorithm [12]. Before the multivariable analysis, the optimal number of clusters is determined by the silhouette value, which is defined as: s¼

n 1 X ½minðaðx; kÞ  bðxÞÞ n i¼1 max½bðxÞ; minðaðx; k ÞÞ

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where a(x,k) and b(x) represent, in cluster k, the average distance of the interclass and intraclass, respectively, and the range of silhouette values is from 1 to 1. Therefore, the maximum silhouette value can ensure better clustering quality. Figure 4 shows that the silhouette value is a function of the number of clustering class k, where k ¼ 3 is optimal for clustering analysis. In addition, three main damage modes for

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Fig. 4 Silhouette value versus number of clustering class k

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composites are associated with matrix cracking, fiber/matrix debonding, and fiber damage, respectively, under the tensile tests [16]. On the basis of the discussion, the classification to decide is regarded as a three-class. To reduce the dimension of the data and visualize the clustering results, AE signals are projected into a 2D space by PCA. The principal components are extracted from four descriptors, which have similarities between AE signals. Figure 5 presents the percentage of the total variability and the change of cumulative variance for the principal components. It can be clearly seen that the cumulative variance of the first two principal components is up to 81%, which reveals that most of the AE information has been covered. The projection of the AE signals on the first two principal components is shown in Fig. 6. The AE signals clustered by the k-means clustering algorithm can be divided into three typical clusters and are marked P-1, P-2, and P-3, respectively. It can be clearly seen that there is barely any overlap for classifying the results for the two principal components. It is found that the homogeneous groups of AE signals can be effectively determined by the PCA method; more importantly, the clustering results also certify the feasibility of reducing the dimensions by the PCA method.

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Figure 7 shows three clusters separated by both the peak amplitude and the frequency. In the initial stage, AE signals of class A with a low frequency are generated and the dominant frequency range is 0–50 kHz. The peak amplitude has a wide range from 40 to 100 dB. Compared with class A, class B has a significant increase in frequency and the dominant frequency range is 50–150 kHz, while the distribution of the peak amplitude is similar. In the case of class C, the distribution of the frequency becomes wider and higher, in a range of 150–400 kHz. However, there is barely seen the difference about the peak amplitude from clusters. In comparison with the peak frequency distribution, the relationship between the clusters and damage modes can be established. Table 1 lists the frequency distributions of the different damage mechanisms from the literature [12, 17]. On the basis of the peak

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frequency distribution of the clusters, classes A, B, and C are associated with matrix cracking, fiber/matrix debonding, and fiber damage, respectively.

3.3

Full-Field Distributions of Displacement

According to the 2D DIC method [18], the image correlation matching operation between the reference image f and the deformed image g is analyzed by the following equation. i  h  0 0  Pm Pm      f g x f x ; y ; y  g i j i j i¼1 j¼1 C ðu; vÞ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffirffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi h   i2ffi     Pm Pm 2 Pm Pm f xi ; y j  f g x0 ; y0  g i¼1

j¼1

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ð2Þ where f(x,y) and g(x´,y´) are defined as the gray value of the reference and deformed images, respectively. The component of fand g is the average gray value of f(x,y) and g(x´,y´). Before obtaining the vertical and horizontal displacement distributions, the speckle images are scanned at 33 pixels/mm. The vertical displacement distributions of the composite specimen at the same incremental load are shown in Fig. 8. The vertical displacement distribution is in a graded distribution. As shown in Fig. 8a–c, as the tensile load increases from 28 to 30 kN, from 38 to 40 kN, and from 48 to 50 kN, the maximum displacement values are 3.254, 2.843, and 2.978 μm, respectively. However, a low AE relative energy appears at this stage, which indicates the damage accumulation in the specimen. When the load increases up to the failure load, the displacement distribution starts to be asymmetrical, as shown in Fig. 8d, and this is accompanied by a rapid increase in AE relative energy, which leads to fracture of the specimen.

Fig. 8 Vertical displacement distributions for the specimen. (a) From 28 to 30 kN. (b) From 38 to 40 kN. (c) From 48 to 50 kN. (d) From 58 to 60 kN

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Fig. 9 Horizontal displacement distributions for the specimen. (a) From 28 to 30 kN. (b) From 38 to 40 kN. (c) From 48 to 50 kN. (d) From 58 to 60 kN

Figure 9 shows the horizontal displacement distributions of the composite specimen. When the tensile load is at a low level, the horizontal displacement distribution is asymmetrical and the maximum displacement value is below 0.2 μm, as shown in Fig. 9a–c. As the tensile load increases from 58 to 60 kN, an obvious change in the horizontal displacement is induced and the maximum displacement value is 0.905 μm in Fig. 9d. The slight change in the displacement fields can well reflect the surface layer deformation and damage in the composite specimen.

4 Conclusion 1. Acoustic emission (AE) signals in composites can be visualized in two-dimensional coordinates by the principal component analysis method to effectively find homogeneous groups among the data sets generated. The kmeans clustering algorithm classifies the AE signals, which are divided into three classes. On the basis of the frequency range, the relationship between clusters and damage modes is established. 2. At different damage modes, AE signals have different characteristics. Fiber damage has a high frequency from 150 to 400 kHz, fiber/matrix debonding corresponds to a medium frequency from 50 to 150 kHz, and the low frequency from 0 to 50 kHz is matrix damage. However, the distribution of the peak amplitude is barely used to distinguish different damage modes. 3. A method combining AE and digital image correlation (DIC) can effectively provide early signs of damage and quantification of progressive damage accumulation in composites. More importantly, the distribution of displacement filed is visual inspection. Therefore, the mutual correlation between AE and DIC plays an important role in structural health monitoring. Acknowledgements The authors gratefully acknowledge financial support received from the National Natural Science Foundation of China (grant no. 11502064) and the Natural Science Foundation of Hebei Province (grant no. E2016201019).

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References 1. Z. Hu, R. Karki, Prediction of mechanical properties of three-dimensional fabric composites reinforced by transversely isotropic carbon fibers. J. Compos. Mater. 49(12), 1513–1524 (2015) 2. J.K. Kim, Y.W. Mai, High strength, high fracture toughness fibre composites with interface control—a review. Compos. Sci. Technol. 41(4), 333–378 (1991) 3. T.C. Henry, C.E. Bakis, Compressive strength and stiffness of filament-wound cylinders. J. Reinf. Plast. Comp. 35(21), 1543–1553 (2016) 4. Q. Wu, F. Yu, Y. Okabe, S. Kobayashi, Application of a novel optical fiber sensor to detection of acoustic emissions by various damages in CFRP laminates. Smart Mater. Struct. 24(1), 015011 (2015) 5. S.K. Al-Jumaili, K.M. Holford, M.J. Eaton, J.P. McCrory, M.R. Pearson, Classification of acoustic emission data from buckling test of carbon fibre panel using unsupervised clustering techniques. Struct. Health Monit. 14(3), 241–251 (2015) 6. F. Cesari, V.D. Re, G. Minak, A. Zucchelli, Damage and residual strength of laminated carbon– epoxy composite circular plates loaded at the centre. Compos. Part A Appl. Sci. Manuf. 38(4), 1163–1173 (2007) 7. M. Gresil, M.N. Saleh, C. Soutis, Transverse crack detection in 3D angle interlock glass fibre composites using acoustic emission. Materials 9(8), 699 (2016) 8. C. Liu, L. Cheng, X. Luan, B. Li, J. Zhou, Damage evolution and real-time non-destructive evaluation of 2D carbon-fiber/SiC-matrix composites under fatigue loading. Mater. Lett. 62 (24), 1163–1173 (2008) 9. M. Shateri, M. Ghaib, D. Svecova, D. Thomson, On acoustic emission for damage detection and failure prediction in fiber reinforced polymer rods using pattern recognition analysis. Smart Mater. Struct. 26(6), 065023 (2017) 10. J.P. McCrory, S.K. Al-Jumaili, D. Crivelli, M.R. Pearson, M.J. Eaton, Damage classification in carbon fibre composites using acoustic emission: a comparison of three techniques. Compos. Part B Eng. 68(5), 424–430 (2015) 11. S.V. Lomov, M. Karahan, A.E. Bogdanovich, I. Verpoest, Monitoring of acoustic emission damage during tensile loading of 3D woven carbon/epoxy composites. Text. Res. J. 84(13), 1373–1384 (2014) 12. L. Li, S.V. Lomov, Y. Xiong, V. Carvelli, Cluster analysis of acoustic emission signals for 2D and 3D woven glass/epoxy composites. Compos. Struct. 116(1), 286–299 (2014) 13. L. Li, S.V. Lomov, Y. Xiong, Correlation of acoustic emission with optically observed damage in a glass/epoxy woven laminate under tensile loading. Compos. Struct. 123(123), 45–53 (2015) 14. X. Wang, S.P. Ma, Y.T. Zhao, Z.B. Zhou, P.W. Chen, Observation of damage evolution in polymer bonded explosives using acoustic emission and digital image correlation. Polym. Test. 30(8), 861–866 (2011) 15. J. Cuadra, P.A. Vanniamparambil, K. Hazeli, I. Bartoli, A. Kontsos, Damage quantification in polymer composites using a hybrid NDT approach. Compos. Sci. Technol. 83(15), 11–21 (2013) 16. C.R. Ramirez-Jimenez, N. Papadakis, N. Reynolds, T.H. Gan, P. Purnell, Identification of failure modes in glass/polypropylene composites by means of the primary frequency content of the acoustic emission event. Compos. Sci. Technol. 64(12), 1819–1827 (2004) 17. R. Gutkin, C.J. Green, S. Vangrattanachai, S.T. Pinho, P. Robinson, On acoustic emission for failure investigation in CFRP: pattern recognition and peak frequency analyses. Mech. Syst. Signal Process. 25(4), 1393–1407 (2011) 18. Y.J. Ma, X.F. Yao, D. Wang, Experimental investigation on mechanical properties of CNT film using digital speckle correlation method. Opt. Laser Eng. 50(11), 1575–1581 (2012)

The Study of Mechanical Behavior of Alloy Structural Steel Based on Dynamic Acoustic Emission Signal Xiaoli Li, Xinbo Chen, and Jinli Sun

Abstract The corresponding relationship between mechanical behavior of 30CrMo steel material and acoustic emission signal was studied by using dynamic acoustic emission signal in this paper. Through the analysis of the parameters of amplitude, ringing count, and duration of acoustic emission signals, we obtained the dynamic acoustic emission characteristics of the specimen during elastic deformation, plastic yielding, hardening, necking, and fracture, which reflect the structural behavior changes in the stretching process. The results of the experiment showed that it was instructive to use acoustic emission signal parameters to study the damage test of the material and the online monitoring of the engineering structural material. Key words Acoustic emission (AE) · Tensile test · 30CrMo · Mechanical behavior

1 Introduction Alloy structural steel usually has high yield strength, tensile strength, and fatigue strength. In the medium-sized machinery manufacturing industry, it is mainly used for the manufacturing of quenched and tempered parts, such as shafts, spindles, as well as high load control wheel, bolts, gears, and so on. Because of the bad operating environment for special equipment such as high temperature, high pressure, high speed, easy to be corrupted, and huge tension, the security condition is complicated, and the damage rate is higher. Currently, the damage of special equipment is detected in conventional nondestructive testing (NDT) methods. These conventional NDT methods can only be used in artificial periodic structural properties of local detection. The location of damage detection mainly depends on the probability of failure and supportive experience, but it cannot achieve the real-time monitoring and online detection; therefore, it does not nip in the bud in the true sense.

X. Li (*) · X. Chen · J. Sun Qingdao Branch, Naval Aeronautical University, Qingdao, Shandong, People’s Republic of China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_19

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Acoustic emission (AE) is a common physical phenomenon that occurs when the material is damaged by external force or internal force. In the process of AE, the transient elastic wave is produced by the rapid release of local source energy in the material. AE technology [1] is the use of acoustic emission instruments to detect, record, and analyze acoustic emission signals and to determine the damage states by acoustic emission signals. Compared with conventional NDT techniques, the signal detected by AE technology is the acoustic emission signal produced by the material damage itself, and its production mechanism and signal characteristics are closely related to the damage monitoring; therefore, AE technology can be used for equipment damage monitoring. In this paper, the special equipment made of steel 30CrMo was used as the research object. The acoustic emission signal was monitored in the static tension process of 30CrMo steel, and the relationship between the AE signals and the tensile state of the 30CrMo steel is studied.

2 Analysis of AE Mechanism in Tensile Process of Metallic Material The metal material experiences two major processes—deformation and fracture during the static tension process. Different mechanical behaviors can be reflected by the numbers, strength, and changes of AE signals produced by the material during the deformation and fracture processes.

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AE Mechanism at Elastic Deformation Stage

Metal deformation has two aspects—elastic deformation and plastic deformation. Elastic deformation is that the part of the deformed material which when the applied load is removed would spring back to its normal shape. The internal lattice produces elastic deformation, but the spacing of the atoms does not exceed the lattice spacing. Within the range of molecular potential energy range, the material will quickly recover to external deformation after unloading. Therefore, there is almost no release of strain energy during the elastic deformation stage. There is no AE signal from the material deformation. In order to avoid noise interference, usually acoustic emission threshold strain is about 60% of the yield strain [2].

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AE Mechanism at Plastic Deformation Stage

When the strain exceeds the threshold strain, the metal material enters the plastic deformation stage. One of the main reasons for the formation of acoustic emission

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sources is the plastic deformation of metals, such as dislocation motion caused by the formation of Luders belt, Bauschinger uneven deformation, twinning, hardening alloy phase deformation, fracture, and so on, which can generate acoustic emission signals [3]. Through experimental analysis on the AE characteristics of deformation process of LiF material, Professor James and Carpenter found that the AE incidence rate (represented by a ringing count rate N ) is proportional to the growth rate of the crystal moving dislocation density, i.e.: dN ¼ 104

dρm dt

ð1Þ

Therefore, if the strain increases at constant strain rate, the occurrence probability of AE will increase from zero to maximum value. Generally, the strain reaches the maximum extent near the yield point of the material, and at the same time, the AE incidence rate appears the peak of counting. After the yield point, the AE incidence rate will decrease with the increase of strain rate until the material enters the stage of machinery strengthening.

2.3

AE Mechanism at Hardening Stage

After the yield stage, because of the piling up of dislocations, the dislocation motion between the lattices becomes difficult, and the deformation resistance of the material has been restored, so the moving becomes difficult. Therefore, if the material continues to deform, the load must be increased. Acoustic emission probability decreases at this stage, because of the piling up of dislocations which caused the dislocation to move slowly and even stagnate [4].

2.4

AE Mechanism at Necking Fracture Stage

In the strengthening stage, the fatigue source was emerged for the stress concentration which was caused by the piling up of dislocation. The 30CrMo steel has good plasticity. After crack nucleation, the crack propagation occurs intermittently and there is a long process before the macro crack develops. The local stress in the material is released slowly, accompanied by the generation of acoustic emission signals until the material begins to lose stability and forms instantaneous fracture. At the moment of fracture, a large amount of energy is released inside the material, and a high intensity acoustic emission signal is produced at the same time. When the material loses its stability and forms an instant breaking, it releases great amounts of energy, which produce high-intensity AE signals [5].

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3 Experiment 3.1

Specimen Processing and Finite Element Simulation

In this experiment, the alloy structure steel 30CrMo was used as the research object, which is commonly used in special equipment. Its mechanical properties were shown in Table 1. The shape and size of the specimen were designed according to aviation industry standard HB 5287-1996 “metal material axial loading fatigue test method,” which was shown in Fig. 1. The stress distribution of the specimen under axial static tension was simulated by ANSYS simulation software. Get different axial tensile force under equivalent stress nephogram, which was shown in Fig. 2. From Fig. 2, it was clearly seen that during the static tension process, the position of the maximum stress concentration in the middle of the specimen position and from the middle to both sides decreases with the diffusion. The greater the load tension force, the greater the stress concentration. With the stress concentration degree increased, the crack may further expand until break. According to the simulation results, this experiment focused on the damage condition of the middle part of the specimen.

3.2

Experiment Parameter Setting and Loading Scheme

The acoustic emission acquisition system used the AMSY-5 type 8 channel acoustic emission detector, and the piezoelectric sensors used the VS150-M model. The Table 1 Mechanical property of 30CrMo steel Performance parameter Value

Yield strength σ s (MPa) 785

Fig. 1 Structure and geometry size of tensile sample

Tensile strength σ b (MPa) 930

Elongation σ s (%) 12

Section shrinkage ψ (%) 50

Fatigue strength σ (MPa) 728

Breaking load F (kN) 918

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Fig. 2 Stress nephogram of tensile sample

Table 2 Parameter setting of acoustic emission detector

Instrument parameter Preamplifier gain (dB) Threshold value (dB) Waveform sampling rate (MSPS/S/s) Bandpass filter band (kHz) Peak definition time PDT (μs) Impact definition time HDT (μs) Impact blocking time HLT (μs)

Setting value 40 46 1 100–200 300 600 1000

frequency range of the piezoelectric sensor is 50 kHz to 1 MHz; the center frequency is 150 kHz. AE signals decayed rapidly during propagation. In order to avoid the loss of signal in the communication process, the sensor is installed in the vicinity of stress concentration region, which is near to the central region. The experimental parameters were shown in Table 2. The experiment equipment installation figures were shown in Fig. 3. The loading system adopted MTS810-500KN fatigue testing machine. After the AE experiment parameters were set up, the loading system began to load with 50 N at the speed of 2 mm/min, and the AE signal was collected until the test finished.

4 Experiment Results and Discussion 4.1

Mechanical Characteristics Analysis

In order to study the AE characteristics of static tensile process of 30CrMo steel, the mechanical behavior of the tensile process was analyzed first. Figure 4 was the figures of the different tensile process of the tensile specimen.

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(a) Placement of the tensile test machine and the sample

(b) Acoustic emission sensor installation

Fig. 3 Test equipment of acoustic emission testing. (a) Placement of the tensile test machine and the sample. (b) Acoustic emission sensor installation

(a) Elastic deformation sample

(b) Necking sample

(c) Fracture sample

Fig. 4 Physical figure of 30CrMo tensile specimen failure. (a) Elastic deformation sample. (b) Necking sample. (c) Fracture sample

The tensile load-time curve of the 30CrMo specimen was obtained in the experiment, which was shown in Fig. 5. According to the analysis method of mechanics of materials, combined with the load-time curve, tensile process was divided into the following four stages: 0–27 s was the pre-loaded elastic stage; 28–40 s was the plastic yield stage; 41–90 s was the hardening stage; and 91–175 s was the local necking stage and 176 s to the end was the stage of fracture.

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Fig. 5 Load curves of 30CrMo tensile specimen

4.2

AE Characteristics of Tensile Damage

The AE events of structural damage were usually judged by the activity and intensity of AE signals. The AE signal amplitude, ringing count, and signal duration can reflect the strength of AE event and also can reflect the activity of AE event. In this paper, the parameters of AE signal amplitude, ringing count, and signal duration were chosen to study the characteristics of the tensile damage of materials. Figure 6 showed the AE spectrogram of the tensile process of 30CrMo. From Fig. 6, it was clearly seen that the spectrum distribution range was very wide; in the 100–400 kHz frequency range, all had energy distribution, the main band was in the 100–170 kHz, and the peak appeared near the 150 kHz. Figure 7 showed the AE parameter history of the tensile process of 30CrMo. From Fig. 7, it was clearly seen that AE events occurred throughout the stretching process. In the different stress stage, the number of AE signals and the amplitude of AE signals were all different. The characteristics of AE signals in different stage were discussed as follow: 1. The pre-loaded elastic stage (0–23 s). Figure 4a showed the specimen in the elastic deformation stage. It was found that the specimen had no obvious elongation, which illustrate that the deformation of the material was resumed after unloading. Figure 7a showed the AE amplitude history. From Fig. 7a, it was clearly seen that it did not produce AE signals during the pre-loading process, except small AE signal which is due to friction between the clamp and specimen. It also showed that the elastic deformation occurred only in the elastic range of the atoms within the material, but no strain energy was released, so almost no acoustic emission signal was produced. The figure of AE counting history (i.e., Fig. 7b) and the figure of duration history (i.e., Fig. 7c) also showed that no significant AE events occurred at this stage.

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Fig. 6 Amplitude history images of acoustic emission

(a) Amplitude history images of acoustic emission

(b) Counts history images of acoustic emission

(c) Duration history images of acoustic emission

Fig. 7 Parameter history images of acoustic emission. (a) Amplitude history images of acoustic emission. (b) Count history images of acoustic emission. (c) Duration history images of acoustic emission

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2. The plastic yield stage (24–40 s). Figure 4b showed the specimen in the plastic yield stage. It was found that the morphology of specimen did not change significantly, but its size increased. This phenomenon indicated that lattice structure slipped because of the internal material movement. As shown in Fig. 7a–c, the AE count increased significantly, and the maximum amplitude reached 52 dB, with a maximum duration of 9000 μs, and the AE events were active at this stage. The reason of this phenomenon was that 30CrMo belongs to polycrystalline metal materials, and the dislocation motion in the stress concentration was irreversible. Dislocation motion led to dislocation slip and dislocation avalanche, which is due to 30CrMo specimen’s internal plastic deformation and energy release. Therefore, the AE signals showed higher amplitude and count. When the yield point is reached, the dislocation motion reaches its maximum, which leads to high amplitude and strong activity of acoustic emission events. 3. The hardening stage (41–90 s). At this stage, there was no obvious deformation from tensile specimen appearance, but its length was obviously longer than that of the previous stage. It showed that plastic deformation intensifies with the increasing of load. The AE parameter history in Fig. 7 showed that in the early hardening stage, AE signal density was still intense, but its amplitude decreased slightly. And in the later stage, the number of AE signals had decreased significantly; the ringing count was at a low level. The reason of this phenomenon was that at the hardening stage, the moving dislocations gradually decreased, which caused plastic deformation difficultly, so the number of AE events was lower than before, but the amplitude appeared the second peak. 4. The local necking and fracture stage (91–176 s). At this stage, there was obvious deformation on the tensile specimen, which was shown in Fig. 6b. And the overall length of specimens increased, until the specimen is broken. The AE parameter history in Fig. 7 showed that the number and amplitude of AE signals were obviously decreased and the signal duration was short. Some scholars believe that when the material necks, the internal deformation of the material changes from axial tension to triaxial tension stress, which makes the material produce great plastic deformation and dislocation movement reach the maximum. But at the same time, the degree of freedom of motion is greatly reduced, and the plasticity becomes extremely poor, resulting in dislocation accumulation. Therefore, in this stage, the number of acoustic emission signals is small, the energy is low, and the activity is poor. Therefore, the AE signal was less, the energy was low, and the activity was very poor. But in the later necking stage, the number of AE signals was increased slightly that the AE signals may be produced from dislocation the second time of stress concentration, which caused the microcrack initiation. Because the fracture signal had burst signal characteristics, the amplitude was high, but the duration was short. When the plastic deformation ability of the material disappears completely, the specimen broke instantaneously and released enormous energy at the same time. At this time, the amplitude of AE signal reached 78 dB, the ring count reached 1900, the duration was very short and the energy was very high.

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5 Conclusion In this paper, the relationship between the AE signals and the tensile state of the 30CrMo steel was studied. It was found that the AE source of the tensile fracture process of 30CrMo steel mainly came from dislocation movement, and the AE signal had obvious change at different tensile stages. The AE signals from different tensile stages reflected the behavior of 30CrMo steel during the tensile process. The experiment results showed that the AE parameters could reflect the behavior of tensile process very well and could reflect the damage mechanism which the mechanical behavior curve could not be reflected. It was instructive to use AE technology to study the damage test of the material and the online monitoring of the material of engineering structure.

References 1. S. Gongtian, Acoustic emission testing technology and its application (Science Press, Beijing, 2015) 2. Z. Xiulin, Mechanical behavior of engineering materials (Northwestern Polytechnical University Press, Xian, 2004) 3. L. Mengyuan, S. Zhen-dong, et al., Acoustic emission detection and signal processing (Science Press, Beijing, 2010) 4. Z. Yihui, Z. Wen-bin, X. Fei-yun, et al., Acoustic emission characteristics of tensile damage test of Q235 steel plate. J. Vibrat. Shock 34(15), 156–161 (2015) 5. Z. Zhongxing, Nondestructive testing and safety assessment of materials (China Standard Press, Beijing, 2003)

Acoustic Emission Characteristic of Ceramic Matrix Composite Under Static Loading Yong Gao, Denghong Xiao, Liang Jin, Bo Jiang, Naitian Li, Quanhong Ye, Xiaohong Zhou, Zongkai Tong, and Fanchao Meng

Abstract The acoustic emission characteristics of ceramic matrix composite under static loading were investigated. The acoustic emission characteristics and the corresponding load ratio were analyzed. The damage mode of the ceramic matrix composite was also analyzed through the AE technology. The experimental results show that lots of AE signals were generated when the ceramic matrix composite is under the action of external load. Besides, it clearly indicated that the AE can be well used as a significant unit for structure health evaluation. Key words Fiber-reinforced ceramic matrix composites · Static test · Acoustic emission

1 Introduction The CMC (carbon fiber-reinforced matrix ceramic) structures are advantageous to thermal structural [1–3] because of their thermal shocking resistance and excellent mechanical properties. Carbon/silicon carbide-reinforced composites (C/SiC) have been used in many high-temperature aerospace fields, such as aeroengine combustor, rocket tail nozzle, and engine blade. Before using such composite materials as loadbearing components of structures, their damage characteristics should be fully understood. When CMC is subjected to external loads, it often exhibits some

Y. Gao School of Energy and Power Engineering, Beihang University, Beijing, People’s Republic of China Beijing Electro-Mechanical Engineering Institute, Beijing, People’s Republic of China D. Xiao (*) · L. Jin · N. Li · Q. Ye · X. Zhou · Z. Tong · F. Meng Beijing Electro-Mechanical Engineering Institute, Beijing, People’s Republic of China e-mail: [email protected] B. Jiang Shenyang Aerospace Xinguang Group Co., Ltd, Shenyang, People’s Republic of China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_20

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nonlinear characteristics, which may be caused by various factors, such as energy dissipation effect, interlaminar delamination, fiber fracture, matrix fracture, and interfacial friction. All of the above damage or micro-mechanical behavior will produce a sudden release of energy, that is, high-frequency elastic waves such as acoustic emission. This kind of acoustic emission wave can be analyzed and characterized by modern acoustic emission technology. With the development of information technology, acoustic emission technology has been successfully applied to damage monitoring and pattern recognition of ceramic matrix composite, and related work has shown that there is a certain correlation between acoustic emission and damage [4–7]. In this paper, acoustic emission (AE) technology is firstly used to monitor the damage process of ceramic matrix composite structures. According to the energy characteristics of AE, the mechanical behavior of structural components at different loading stages is analyzed. The research results have important reference value for revealing the damage characteristics of carbon/silicon carbide composites under complex mechanical environment.

2 Acoustic Emission Testing 2.1

Measurement

In order to record as many acoustic emission signals as possible, the full-waveform acoustic emission testing equipment was used to collect the signals at a sampling frequency of 3 Hz. Before the experiment started, the lead-breaking analog acoustic emission test was carried out, that is, the process of breaking pencil lead was used to release the analog acoustic emission signals. At the same time, the post-processing software is used to display the AE signal in real time (Fig. 1). Parameter selection of ceramic matrix composite under static loading is shown in Table 1.

Fig. 1 Acoustic emission testing of ceramic matrix composite under static loading

Load AE sensors AE source

C/SiC plate

Acoustic Emission Characteristic of Ceramic Matrix Composite. . . Table 1 Parameter selection of ceramic matrix composite under static loading

Parameters Channel Threshold value Preamplifier gain PDT HDT HLT

217 Value 8 40 dB 40 dB 50 μs 100 μs 300 μs

Fig. 2 AE signals collected by the eight sensors

2.2

AE Sensor Arrangement

Eight sensors are arranged on the front side and distributed evenly over the entire wall panel for damage monitoring and positioning. On the back, there are two distinct interfaces due to the opening, so that a sensor is arranged at the center of each area to carry out the monitoring of the damage and complement each other with the front five sensors. AE signals were collected by those eight sensors which are shown in Figs. 2 and 3. From Fig. 4, it can be seen that few signals existed when the frequency is larger than 400 kHz. A high-pass filter was taken to analyze the AE signals. Figure 5 showed the signals which were processed after filtering. The signal for each sensor is enlarged as shown in Fig. 6. From the signal filtered by 400 kHz high-pass processing, it can be found that the signal with frequency more

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50 100 150 200 250 300 350 400 450 500 550 600 650 700

Fig. 3 Relationship between the energy and the arrival time of AE signals obtained by 4 # sensor

than 400 kHz obtained from 1 #, 2 #, 3 #, and 5 # sensor is little, which amplitude is below 100 mV. Besides, those signals picked up by 1 #, 2 #, 3 #, and 5 # sensor are relatively large, which spectrum distribution is shown in Fig. 7. The difference of AE signals obtained by different AE sensors can be obtained by analyzing the AE signals collected by these sensors and can refer to the difference between the important parameters of arrival time of each signal. Table 2 is the AE signal received by eight AE sensors and filtered by 20–90 kHz band-pass filter. The acoustic emission signal after filtering is counted mainly according to the two parameters of acoustic emission event and energy. Table 3 gives the statistical results of acoustic emission events and energy in different frequency ranges. From Table 3, it can be seen that the number of acoustic emission events with a frequency of 400 kHz or more is low, and the energy is very low. The number of AE events with frequency between 90 and 400 kHz is the largest.

3 Conclusion The acoustic emission test of the composite under static load conditions can effectively and clearly reveal the acoustic emission characteristics and material damage characteristics of the material during the damage process under the static test

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(a) A part of AE data in time domain

(b) The spectrum of the data

Fig. 4 The corresponding spectrum of AE data. (a) A part of AE data in time domain. (b) The spectrum of the data

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Fig. 5 The signal obtained by 400 kHz high-pass filter

Fig. 6 A zooming in view of the AE signal obtained by 400 kHz high-pass filter for channel 1

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(a) Relationship between arrival time and energy for the signal4 # sensor after 90KHz-400KHz filtering

17E+2 16E+2 15E+2 14E+2 13E+2 12E+2 11E+2 10E+2 90E+1 80E+1 70E+1 60E+1 50E+1 40E+1 30E+1 20E+1 10E+1 00E+0

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(b) Relationship between arrival time and energy for the signal4 # sensorafter20KHz-90KHz filtering

Fig. 7 Relationship between arrival time and energy for the signal 4 # sensor. (a) Relationship between arrival time and energy for the signal 4 # sensor after 90–400 kHz filter. (b) Relationship between arrival time and energy for the signal 4 # sensor after 20–90 kHz filter

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Table 2 AE waves are filtered by 20–90 kHz bandpass filtering

Channel 1 2 3 4 5 6 7 8

AE events 63 61 99 244 84 127 259 258

Energy (mV  ms) 4981 4848 8631 16,754 4088 11,441 22,793 26,821

Table 3 Number and energy of the acoustic emission events received by the eight sensors after 20– 90 kHz band-pass filtering Channel 1 2 3 4 5 6 7 8

AE events 20–90 kHz 63 61 99 244 84 127 259 258

90–400 kHz 136 101 366 922 149 356 572 679

>400 kHz 1 1 6 38 3 8 5 25

Energy (mV  ms) 20–90 kHz 90–400 kHz 4981 4895 4848 3728 8631 13,409 16,754 24,609 4088 2218 11,441 15,317 22,793 19,852 26,821 25,904

>400 kHz 7 8 15 138 13 102 276 179

conditions. This work can provide the basis for the health monitoring of the material. Some conclusion can be drawn as follows: 1. In the analysis of AE signal obtained from static test, the signal with the frequency below 20 kHz can be removed to enhance noise ratio of the damage AE signal. 2. The structure has two large acoustic emission bursts in the static experiment. During the first loading, the assembly surface is fretted, and the material is non-expansive. And the actual damage to the acoustic emission cumulative energy map is significantly different. 3. The acoustic emission cumulative energy is not a typical cumulative energy distribution. Therefore, it cannot be assumed that there are two large acoustic bursts in the static experiment. During the first loading, the assembly surface is freak, and the AE signals stand for the extended damage information. Acknowledgment This work was finically supported by the National Science Foundation of China (Grant No. 51605459).

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References 1. F. Breede, D. Koch, E. Maillet, et al., Modal acoustic emission of damage accumulation in C/CSiC composites with different fiber architectures. Ceram. Int. 41(9), 12087–12098 (2015) 2. S. Schmidt, S. Beyer, H. Immich, et al., Ceramic matrix composites: a challenge in spacepropulsion technology applications. Int. J. Appl. Ceram. Technol. 2(2), 85–96 (2005) 3. F. Breede, R. Jemmali, H. Voggenreiter, et al., Design and testing of a C/C-SiC nozzle extension manufactured via filament winding technique and liquid silicon infiltration, in Design, Development, and Applications of Structural Ceramics, Composites, and Nanomaterials: Ceramic Transactions, ed. by D. Singh, vol. 244, (Wiley, Hoboken, NJ, 2014), pp. 1–14 4. H. Rivers, D. Glass, Advances in hot structure development, in Thermal Protection Systems and Hot Structures, ed. by K. Fletcher, (ResearchGate, Berlin, 2006) 5. J.D. Corso, B. Cheatwood, et al., Advanced high-temperature flexible TPS for inflatable aerodynamic decelerators, in AIAA Aerodynamic Decelerator Systems Technology Conference and Seminar, ed. by AIAA, (AIAA, Reston, 2011), pp. 139–161 6. A.R. Brewer, Edgewise Compression Testing of STIPS-0 (Structurally Integrated Thermal Protection System). Technical Report, NASA/CR-2011-217161 (NASA, Hanover, MD, 2011) 7. D. Zhu, M. Halbig, M. Jaskowiak et al., Property evaluation and damage evolution of environmental barrier coatings and environmental barrier coated SiC/SiC ceramic matrix composite sub-elements, in 38th Annual Conference on Composites, Materials and Structures (2014)

Part IV

Structure

Evaluation of Damage in RC Bridge Decks Reinforced with Steel Plates by AE Tomography Yiming Feng, Tomoki Shiotani, Yoshikazu Kobayashi, Takahiro Nishida, Hisafumi Asaue, Katsufumi Hashimoto, and Shigeru Kayano

Abstract The aging of infrastructures has been one of the serious problems around the world, and innovative maintenance systems are desired. In Japan, an enormous budget is going to be paid for renewal projects of civil engineering structures, which were constructed during the period of high economic growth in 1960s. Some of severe cases are found in the RC bridge decks, peculiarly reinforced by steel plates on the bottom, as their conditions are difficult to be evaluated by the visual inspection or conventional NDEs. In order to implement an appropriate maintenance or renewal for this type of bridge decks, an accurate and practical damage evaluation system for RC bridge decks reinforced with steel plates should be established. In present study, internal damages of RC bridge decks reinforced by steel plate were evaluated by the analysis of the acoustic emission (AE) activities and AE tomography to contribute to the decision-making if they shall be repaired, reinforced, or replaced. Through the results of AE tomography, it is concluded that the elastic wave velocity can represent the internal damage condition which cannot be confirmed by visual observation. Thus, the AE measurement and the tomographic approach could evaluate the existent or developing damages inside the RC bridge decks with steel plates. Key words RC bridge decks · Steel plate reinforcement · AE source location · AE tomography · Internal damages

Y. Feng (*) · T. Shiotani · T. Nishida · H. Asaue · K. Hashimoto · S. Kayano Graduate School of Engineering, Kyoto University, Kyoto, Japan e-mail: [email protected] Y. Kobayashi Department of Civil Engineering, Nihon University, Tokyo, Japan e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_21

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1 Introduction Load-carrying capacity and durability of reinforced concrete (RC) decks of road bridges in Japan constructed in old design standards are lower than that of today [1]. One of the most popular countermeasures for strengthening RC bridge decks was steel plate bonding. For example, urban highways in Kansai area in Japan have been strengthened by reinforced steel plates. As shown in Fig. 1, steel plates are attached under the RC decks through the attachment of anchor bolts and epoxy resin. Reinforced steel plates greatly improve bending rigidity and fatigue durability of bridge decks. In this case, however, damages on the decks cannot be visually observed from outside. Furthermore, moisture supplied to the road surface stays in bridge decks, which may sometimes accelerate internal corrosion or corrosion of steel plates. Therefore, the effective detection method for internal damage or surface cracks in RC bridge decks reinforced with steel plates is demanded. Acoustic emission (AE) is an elastic wave generated due to crack occurrence, growth, and nucleation, which are referred to as primary AE activity, while the emissions due to existent cracks’ reversible motion induced by internal stress distributions are referred to as secondary AE activity [2]. In this study, secondary AE activities generated in RC bridge decks were detected and analyzed by parameters such as wave velocity and, as for the elastic wave velocities within bridge decks, the area of lower velocities indicated as the deterioration or damage of the concrete.

Fig. 1 Reinforced steel plates and anchor bolts bonded on the bottom of RC deck

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2 AE Tomography 2.1

AE Tomography Method

AE tomography technique is developed by Shiotani and Kobayashi [3, 4, 9, 10]. In AE tomography, the region analyzed is divided into finite-element meshes. The slowness (reciprocal of the velocity) is calculated and assigned to each mesh, and finally a contour of the velocity distribution is obtained. In other words, slowness is given to each element of the model, and the internal soundness of the targeted area can be estimated. Firstly, each element of the area is given a homogeneous elastic wave velocity distribution of 4000 m/s as an initial value. Then source locations of AE activities are performed based on the arrival time difference of AE waves among sensors, and the elastic wave velocity distribution is identified by using the results of AE source locations. By iterating this process until the difference between observation arrival time and theoretical arrival time become sufficiently small, the results of elastic wave velocity distribution can be obtained. In the elastic wave velocity distribution, lowervelocity areas are regarded as those of severe deteriorated condition, while highervelocity area shows sound condition.

2.2

AE Measurement in RC Bridge Decks

In general, it is considered difficult to install the sensors and measure on the concrete of RC decks reinforced with steel plates directly, because air gaps between reinforcing steel plate and concrete is sometimes observed, resulting in difficulty to install the sensors onto the steel plate reinforced. In this study, anchor bolts, which are transitionally used until the time to confirm the adhesiveness between the concrete slab and the steel plate, are utilized as a wave-guide to detect AE generated in RC decks, and sensors are installed at the ends of anchor bolts by screws and magnets. Figures 2 and 3 show the outline of fixing and installing the sensors. Thus, the secondary AE activities, which are generated inside the RC bridge decks by traffic loads, are detected by accelerometers put on the edge of anchor bolt placed on the bottom surface of the steel plate. Then the elastic wave velocity distribution will be obtained by the AE tomography method. In this study, 32 piezoelectric accelerometers (707IS, TEAC) and two wideband recorders (WX-7000, TEAC) are used for measuring a part of the RC bridge deck in urban highway in Kansai area. The outline of the targeted bridge deck and the arrangement of sensors are shown in Fig. 4. The sampling rate is set as 200 kHz, while the range of the frequency response of sensors is from 3 Hz to 20 kHz. Since the measurement system is based on a streaming method, the process of extracting meaningful AE waves from the raw data is necessarily conducted. Firstly, the data is converted to text, and when the amplitude of waves exceeds the set threshold value, a

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Fig. 3 The picture of sensors installation

Fig. 4 The measured in-situ bridge deck and the arrangement of sensors

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waveform of 5 ms centered the time of threshold- crossing will be extracted. Secondly, the arrival time is picked up by means of AIC [5] from the waveform. This process is done for each sensor, and then the arrival time of an AE hit is extracted entirely. Thirdly, by grouping AE hits whose differences of arrival time (AE event duration) is within 1 ms as AE events, the final data is obtained. In this way, sets of input data for the AE tomography is prepared as the AE sources and the arrival time contributin the each of AE sources in every potential combinations of the sensors.

3 Results of Wave Velocity Distribution Due to AE Tomography As described, in this study, anchor bolts are inserted into the concrete on the back side of reinforcing steel plates. And the elastic waves, which are generated inside the bridge decks, propagate through various paths until they are detected by the sensors that are installed at the end of anchor bolts. Therefore, it is considered difficult to analyze how anchor bolts affect the propagation of elastic waves in three dimensions. This study thus only aims to evaluate the plane of RC bridge decks by two-dimensional AE tomography and focuses on planar spread of internal cracks. The result of elastic wave velocity distribution analyzed by AE tomography method is shown in Fig. 5. From the figure, the area of the velocity smaller than 2250 m/s of velocity was identified, and this area was considered as a severe damaged area. On the other hand, AE source locations were also calculated and shown in Fig. 5 as blue plots. The amount of AE sources, which are generated from cracks’ friction, might be illustrated as the progress of the degradation of bridge decks.

Fig. 5 AE source locations and the velocity distribution by AE tomography

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Fig. 6 Observed horizontal cracks of the cutoff deck

4 Verification of Damage in RC Bridge Deck After the measurement of the RC bridge deck reinforced with steel plates by AE tomography, the deck (1.5 m  4.1 m) was cut off. As shown in Fig. 6, horizontal cracks were confirmed at the left-side surface and bottom-side surface of the cutoff bridge deck, which is roughly in agreement with the low-velocity area shown in Fig. 5. In order to verify the result of the AE tomography, the damage of the actual cutoff bridge deck was examined by cored holes injected by red epoxy resin [6]. Specifically, a holes of 5 mm in diameter was drilled in eight places as shown in Fig. 5 for the first, and red resin was injected in these holes. After hardening of the resin, a larger holes then previously were drilled at the same eight places (three places are 9 mm in diameter, five places are 10.5 mm in diameter), and the resin injected area was regarded as cracked by high-resolution endoscope in the concrete deck. Figure 7 shows the example of the confirmation of the horizontal cracks by the endoscope. As the results, it was confirmed that large amount of injected resin has entered into existent horizontal cracks and has been observed in all three holes of low-velocity area. This result matches quite well the result of AE tomography. On the other hand, any injected resin was not confirmed at three places of high-velocity area illustrated by the velocity distribution.

5 Evaluation of Fatigue Damage by Wheel Loading Test 5.1

Wheel Loading Program

In order to simulate cracks actually observed in the cutoff RC bridge deck due to fatigue, repeated loadings with a steel wheel was conducted as shown in Fig. 8. As

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Fig. 7 Horizontal cracks observed by high-performance endoscope

Fig. 8 Wheel loading apparatus

shown in Fig. 9, dimensions of the cutoff RC deck are 2690 mm in length, 950 mm in width, and 170 mm in thickness. And new concrete with the same condition as the cutoff RC deck was placed surrounding the cutoff RC deck, whose resultant dimensions are 5460 mm in length and 2060 mm in width. The steel wheel with 560 mm width and 300 mm in diameter can be applied with the load up to 400 kN in the case of repeated loading and 400 kN in the case of static loading [7, 8]. The steel wheel runs at the center of the span repeatedly, of which the repetition rate is set at 15 rpm in this experiment. In this study, stepwise cyclic loadings are conducted based on the

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Fig. 9 Dimensions of specimen and locations of sensors

Fig. 10 Loading program

loading program as shown in Fig. 10. In the loading program, the loads of 157, 177, 196, 216, and 235 kN were repeatedly applied for each 40,000 times running, respectively.

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Fig. 11 AE sensors installation

5.2

Measurement Conditions

In this study, the AE tomography analysis is again applied by employing elastic wave excitations. As illustrated in Fig. 9, 18 AE sensors of 60 kHz resonance and 18 piezoelectric accelerometers are installed at 36 locations alternately on the bottom of the specimen. The AE sensors’ installation is shown in Fig. 11. In this experiment, the excitations were driven by hammers of 10 mm and 30 mm in diameter, respectively, at random locations on the top surface of the specimen. The wheel loadings were applied at the center area of 560 mm  4600 mm between the red lines as shown in Fig. 9.

5.3

Results of Wheel Loading Test

In order to analyze the velocity distributions of the specimen after 40,000 time cycles on each load, AE tomography analysis was conducted in the region of 3.2 m  1.2 m of the specimen, which is shown as the blue rectangle in Fig. 9. The results of elastic wave velocity distributions of the RC bridge deck specimen after the wheel loading test on each load step, are shown in Fig. 12. From the figure, it is apparent that the areas of the velocities lower than 2750 m/s are progressing as the applied loads’ increase, especially intensively observed at the area of left side of the measurement region. After 40,000 time cycles of 196 kN load, the areas of velocities lower than 2250 m/s are remarkably observed.

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Fig. 12 Velocity distributions during the wheel loading test

6 Conclusions It was confirmed that AE source location and AE tomography detected the damaged area in concrete decks reinforced with steel plates, which was difficult to find by visual inspection or conventional NDTs. In addition, the reliability of the above method was confirmed by comparing the results of observing the internal damage from cutoff bridge decks by core drilling test. And with the AE tomography method, the change of velocity distributions and the progress of fatigue damage due to wheel loading test are able to be demonstrated clearly. In the future, the effective sensor arrangement and improvement of resolution of AE tomography results will be investigated.

References 1. N. Ogura, H. Yatsumoto, T. Shiotani, A study on evaluation of damage in concrete of reinforced concrete slabs bonded with steel plates utilizing temporary set anchors, in 2016 KSMI A Celebration of the 20th Anniversary of the Founding International Conference, pp. 779–802 (CD-ROM 843/887, 2016) 2. T. Shiotani, T. Nishida, H. Asaue, K. Watabe, H. Miyata, Evaluation of fatigue damage for RC bridge deck with elastic wave velocity and AE activity, in 2016 KSMI A Celebration of the 20th Anniversary of the Founding International Conference, pp. 803–805 (CD-ROM 847/887, 2016)

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3. Y. Kobayashi, T. Shiotani, H. Shiojiri, Damage identification using seismic travel time tomography on the basis of evolutional wave velocity distribution model, in Proceedings of Structural Faults and Repair 2006 (CD-ROM) (2006) 4. T. Shiotani, S. Osawa, S. Momoki, H. Ohtsu, Visualization of damage in RC bridge deck for bullet trains with AE tomography. Adv. Acous. Emiss. Technol 158, 357–368 (2014) 5. S. Kayano, T. Shiotani, T. Nishida, K. Hashimoto, T. Miyagawa, Application of AE Tomography for Evaluation Damage in Steel Plate Reinforced RC Slabs (ResearchGate, Berlin, 2017) 6. T. Nishida, T. Shiotani, H. Asaue, A. Sagradyan, Evaluation of internal defects of reinforced concrete columns by means of AE tomography, in 58th Acoustic Emission Working Group Meeting, p. 13(S3-5) (2016) 7. T. Nishida, T. Shiotani, H. Asaue, T. Maejima, Y. Kobayashi, Damage evaluation of RC bridge deck under wheel loading test by means of ae tomography. Progr. Acous. Emiss. XVIII, S26– S31 (2017) 8. T. Shiotani, T. Nishida, H. Asaue, K. Hashimoto, S. Kayano, Y. Tanaka, T. Maeshima, Y. Kobayashi, Advanced NDT contributing performance evaluation of civil structures, in 12th World Congress on Engineering Asset Management & 13th International Conference on Vibration Engineering and Technology of Machinery (2017) 9. Y. Kobayashi, T. Shiotani, Computerized AE Tomography, Innovative AE and NDT Techniques for On-Site Measurement of Concrete and Masonry Structures, State-of-the-Art Report of the RILEM Technical Committee 239-MCM (Springer, Berlin, 2016), pp. 47–68 10. T. Shiotani, N. Okude, S. Momoki, Y. Kobayashi, Proposal of assessment method for infrastructures by AE tomography, Proceedings of 2011 National Conference on Acoustic Emission, 39–42, 2011 (in Japanese)

Damage Quantification Using an Improved b-Value for Concrete Slabs T. Shiotani, C. Granier, and K. Hashimoto

Abstract Quantitative evaluation of concrete bridge decks is in high demand to establish a rational maintenance program. Visual inspections, sounding with tapping, or core samples have so far been used to determine the level of damage in the maintenance program. As these conventional methods have certain drawbacks (e.g., invisibility of bottom plate-reinforced slabs and difficulty of access on high elevated bridges), the results depend greatly on the operators’ skills, a destructive approach, and some points’ information can not reflect the whole of the deck, in turn. Accordingly, the authors have studied other techniques that evaluate the overall members’ damage by means of elastic techniques with use of acoustic emission (AE) and several tomographic approaches. In AE tomography, for example, concrete properties can be represented by elastic wave velocities; thus, partial damage can be visualized as a small-velocity area. With use of this technique, detailed investigations could be reasonably implemented; however, overall evaluation exhibiting the integrity of a unit of a bridge panel, which is required to establish repair and maintenance programs, would not be obtained. Therefore, in this study, an overall representative index for bridge panels is studied with use of an improved b-value. Key words Improved b-value · Concrete slab · Tomography · Damage assessment

1 Introduction Road infrastructures have been constructed intensively since the 1960s, and many in Japan are now reaching the end of their life-span of 50 years. Accordingly, the proportion of those aged more than 50 years will increase from 18% in 2013 to 43% in 2023 [1]. Replacement of those aging infrastructures with new ones would be the most ideal measure; however, because of shrinkage in taxation revenue, leading to a decrease in the construction budget, life-prolonging tactics appear to be the most T. Shiotani (*) · C. Granier · K. Hashimoto Laboratory on Innovative Techniques for Infrastructures, Kyoto University, Kyoto, Japan e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_22

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adaptable option for those existing aging infrastructures. For new structures, proactive maintenance from the early damage phase is widely recognized as the most costeffective life cycle scenario; however, techniques for detecting early damage and corresponding repair methods are yet to be developed, and substantial proactive maintenance treatment has so far not been applied to new infrastructures. Among such infrastructures, concrete bridge decks pose many difficult problems, resulting in high maintenance costs. Specifically, it has been planned by three major expressway companies that more than 90% of the renewal budget over a period of 15 years will be used for bridge decks. Reinforced concrete (RC) bridge decks are such important components of road infrastructures to maintain that many organizations have intensively studied their deterioration mechanisms, countermeasures against deterioration, and so forth [2–5]. The authors have studied acoustic approaches—namely, ultrasonic/seismic tomography [6, 7] and AE tomography [8–11]. In AE tomography, concrete properties can be represented by elastic wave velocities; thus, partial damage can be visualized as a small-velocity area. With use of this technique, detailed investigations could be reasonably implemented; however, overall evaluation exhibiting the integrity of a unit of bridge panel, which is required for establishment of a repair and maintenance program, would not be obtained. The b-value is well known as an earthquake precursor [12], as well as a damage index characterizing failure mechanisms or fracture conditions [13–16]. In this study, an overall representative index for bridge panels is assessed with use of the b-value—namely, an improved b-value, which has been proposed by one of the authors (Shiotani).

2 Tomographic Approaches Elastic wave tomography and AE tomography are methods used for addressing the deterioration of materials or structures. Through use of tomography, distributions of elastic wave parameters such as the velocity are obtained on the basis of the elastic wave sources. In elastic wave tomography, the location and excitation time of the source are both known, whereas both are unknown in AE tomography. Specifically, tomography evaluates concrete characteristics by using some peculiar elastic wave features in each set element over the structure on the basis of variations in elastic wave parameters through propagation—e.g., the velocity, frequency, and amplitude. Among the parameters, the elastic wave velocity can be linked to the modulus of elasticity, which itself varies depending on the presence of internal damage such as cracks or voids. Thus, it can be reasonably assumed that a small elastic wave velocity means a higher level of deterioration. Accordingly, the wave velocity can be a good indicator of the internal condition of concrete structures; therefore, it has been well used to evaluate deterioration.

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3 Seismic b-Value and Improved b-Value In seismology, it is well known that small-magnitude quakes occur more frequently than large-magnitude ones. The relation of the number of quakes as a function of the magnitude is represented by the Gutenberg–Richter equation, approximated by a linear function on both the logarithmic scale of the number of quakes and the scale of energy—namely, seismic magnitude, where the gradient of the approximated line is referred to as the b-value in the cumulative frequency and the m-value in the differential frequency of quakes. The b-value and m-value have been studied in relation to the main shock in earthquakes, and many reports on the values, which can be a precursor of large earthquakes, can be found in the literature (e.g., see Smith [12]). To apply the b-value to progressive failure evaluation of civil engineering materials, Shiotani has proposed an improved b-value (hereafter referred to as the ibvalue) in the field of AE, using statistical values of the peak amplitude distributions [13]. The ib-value has been applied successfully to evaluate failure processes in soil materials [17] and concrete materials [18]. In ib-value analysis, the amplitude range is obtained from statistical values. The formula of the ib-value is given as follows: Improved b-value ¼

logN ðμ  α1 σ Þ  logN ðμ  α2 σ Þ : α1 þ α2

ð1Þ

where N is the cumulative number of AE events; and μ is the mean and σ is the standard deviation of the amplitude distribution, respectively; and α1 and α2 are empirical constants. It is noted that to determine the proper number of the amplitude data, which is necessary for calculating and, a constant number between 50 and 100 events of AE amplitudes shall be employed. It is well known that the ib-value tends to increase when microfailure occurs predominantly than of macrofailure, and a sudden drop to a small value can be found when macrofailure is generated.

4 In Situ Application 4.1

Experimental Condition

Two real RC bridge deck panels of 235 mm thickness were selected as study targets that had deterioration such as rebar corrosion, breaking by salt damage, and deck fatigue, as shown in Fig. 1. It was found that panel A was heavily deteriorated, whereas there was only minor damage in panel B. In each panel, 15 AE sensors of 30-kHz resonance were set on the bottom side of the panels. Details of the arrangement of AE sensors can be found in Fig. 2. AE activities under normal traffic loadings were monitored with a 36-channel AE monitoring system (Express-8, PAC) with a 1-MHz sampling rate for almost 1 week, resulting in a total number

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700mm 700mm

750mm 700mm

Panel A (Heavy damage)

Panel B (Minor damage)

Fig. 1 Overview of a bridge monitoring site and two deteriorated reinforced concrete decks

Fig. 2 Details of the sensor arrangement on panel B (unit: mm)

of 53,800,520 AE events. Using these AE events, acquired AE tomography analysis was carried out. After the AE measurement, the panels were cut off from the deck to be replaced with new ones, and conventional elastic wave tomography and core sampling of several locations were subsequently performed on the cut-off panels. Details can be found in the literature [19].

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Results

In ib-value analysis a minimal number of 50 AE events are required to calculate the ib-value, as previously shown; however, for the seriously deteriorated panel A, because of energy attenuation affected by remarkably developed cracks, a sufficient number of meaningful AE events (i.e., more than 50 in one element of the entire set of 363 elements) could not be obtained; therefore, panel B showing minor damage is only exhibited hereafter. The velocity distributions and photos of core samples retrieved are presented in Fig. 3. With regard to the velocity distribution, the center area in the middle layer at 0.117 m in the chart shows remarkable deterioration, as small velocities of less than 3000 m/s are obtained. For the areas toward both sides, large velocities of more than 3000 m/s are observed, and these areas appear to be more intact than the central area. For the core observation, laterally developed cracks (denoted by red frames in Fig. 3) obviously exist in the central area, whereas no visible damage in the cores can be found in the large-velocity areas. Accordingly, small-velocity areas of less than 3000 m/s are regarded as seriously deteriorated, including well-developed lateral cracks. In the ib-value analysis, the overall trend for each deck is depicted in Fig. 4. It should be noted that the amplitude employed to be analyzed in the ib-value analysis is the amplitude at the source; i.e., source amplitudes are estimated using the relation between the amplitude obtained at the sensor and the propagation distance of all of the arranged sensors. Again, panel A is seriously deteriorated, while only minor damage is assessed in panel B by visual inspection of the bottom surface. In the amplitude distributions shown in the chart, a gentler slope can be found in panel A than in panel B, suggesting a larger degree of damage in panel A than in panel B. This accords pretty well with the visual inspection.

Fig. 3 Velocity distributions in panel B and core samples retrieved from the circular points indicated. The z-axis in the chart shows the depth direction from 0 at the surface

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Panel A

Panel B

Fig. 4 Amplitude (A) distribution of acoustic emission (AE) activity for two decks

The ib-values corresponding to the divided areas in the tomography are shown in Fig. 5 with photographs of core samples. Note, again, that only panel B is discussed, in consideration of the need for a minimum of 50 AE data in each element. As shown in the chart, an ib-value smaller than 0.04 implies serious deterioration, while a value larger than about 0.05 indicates no macroscopic damage. Finally, two results of the AE tomography and the ib-value analysis are depicted in Fig. 6. Although a detailed discussion is not possible, because of the difference in the analysis resolution, the areas evaluated as seriously damaged on the basis of a small velocity accord well with the reddish areas of the ib-value, exhibiting values smaller than 0.04 in Fig. 4. In Table 1, the AE parameter trends as fracture progress are listed in the case of specific concrete beams simulating wave guides composed of rebar and concrete, idealized for rock slope AE monitoring [20]. As shown in the table, an ib-value decrease to 0.04 demonstrates the failure condition of the final stage of bending or the final stage of slipping, resulting in serious damage to the concrete beam; therefore, the ib-value obtained from the damaged areas of the RC deck is identical to that in the previous report on concrete materials. In conclusion, it is confirmed that the ib-value can be an index to demonstrate damage to concrete bridge decks.

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Fig. 5 Improved b-values in each element and several retrieved cores

Fig. 6 Comparison between the results of acoustic emission (AE) tomography and improved bvalues (ib-values)

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Table 1 Conventional evaluation of the improved b-value (ib-value) relating to failure conditions AE parameters Count Energy 30 50 50–100

30

100–200 200–300 300

ib-value Increase up to 0.06

Grade 10

Decrease down to 0.04 Increase up to 0.06

5–10

Fracture levels estimated I II III

0–5

IV

Decrease down to 0.04

V VI

Fracture conditions expected E in B, (mortar crack) I in B (bond crack) F in B (crack coalescence) F in B and E in S (nucleation of shear crack) I in S (macroscopic crack) F in S (slip)

AE acoustic emission, B bending in failure mode, E early failure stage, F final failure stage, I intermediate failure stage, S shear in failure mode

5 Conclusion In this study, the damage evaluated by the velocity in acoustic emission (AE) tomography and the improved b-value (ib-value) obtained from the AE amplitude distributions are compared to verify them by visual observation of core samples. Through this study, it is concluded that small-velocity areas of less than 3000 m/s and those with ib-values smaller than 0.04 agree quite well with the areas exhibiting seriously damage. With regard to the damage index of the ib-value, it is concluded that overall evaluation showing the integrity of a unit of bridge panel (see Fig. 4), which is required in order to establish repair and maintenance programs, as well as local evaluations (see Fig. 5), are both possible. It is noted, however, that a minimum of 50 AE events is required to obtain a quantitative ib-value, and so its application is limited to less damaged areas rather than being applicable to quite seriously damaged ones. Acknowledgements This study was conducted as part of the Road Infrastructure Monitoring System (RIMS) project with the support of the New Energy and Industrial Technology Development Organization (NEDO). The financial support of NEDO is greatly appreciated.

References 1. Ministry of Land, Infrastructure, Transport and Tourism [MLIT], Annual report 2015 on maintenance of roads, Japan (MLIT, Tokyo, 2015). (in Japanese) 2. A.C. Estes, D.M. Frangopol, Updating bridge reliability based on bridge management systems visual inspection results. J. Bridg. Eng. 8(6), 1–9 (2003) 3. M. Scott, A. Rezaizadeha, A. Delahazab, C.G. Santosc, M. Moored, B. Graybeale, G. Washer, A comparison of nondestructive evaluation methods for bridge deck assessment. NDT E Int 36 (4), 245–255 (2003)

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4. N. Gucunski, A. Maher, H. Ghasemi, Condition assessment of concrete bridge decks using a fully autonomous robotic NDE platform. Bridg. Struct. Assess. Des. Constr. 9(2/3), 123–130 (2013) 5. Y. Tanaka, Y. Takahashi, K. Maekawa, Remaining fatigue life assessment of damaged RC decks—data assimilation of multi-scale model and site inspection. J. Adv. Concrete 15, 328–345 (2017) 6. R.S. Schechter, R.B. Mignogna, P.P. Delsanto, Ultrasonic tomography using curved ray paths obtained by wave propagation simulations on a massively parallel computer. Journal of Acoustic Society of America 100(4), 2103–2111 (1996) 7. International Society for Rock Mechanics [ISRM], Suggested methods for seismic testing within and between boreholes. Seismic Testing Suggested Methods (ISRM, Salzburg, 1998), pp. 449–472 8. K. Katsuyama, M. Seto, T. Kiyama, M. Utagawa, Three dimensional AE tomography for image processing of the deteriorated material. JSSE 1992, 321–326 (1992). (in Japanese) 9. F. Schubert, Basic principles of acoustic emission tomography. Deutsche Gesellschaft für Zerstörungsfreie Prüfung [DGZfP], in Proceedings of BB 90-CD, European Working Group on Acoustic Emission [EWGAE] 2004, Lecture 58 (2004) 10. T. Shiotani, N. Okude, S. Momoki, Y. Kobayashi, Proposal of assessment method for infrastructures by AE tomography, in Proceedings of the 2011 National Conference on Acoustic Emission, pp. 39–42 (in Japanese, 2011) 11. Y. Kobayashi, T. Shiotani, Seismic tomography with estimation of source location for concrete structure, in Structural Faults and Repair 2012, CD-ROM (2012) 12. W.D. Smith, The b-value as an earthquake precursor. Nature 289(5794), 136–139 (1981) 13. T. Shiotani, K. Fujii, T. Aoki, K. Amou, Evaluation of progressive failure using AE sources and improved b-value on slope model tests. Progr. Acous. Emiss. 7, 529–534 (1994) 14. S. Colombo, I.G. Main, M. Forde, Assessing damage of reinforced concrete beam using “bvalue” analysis of acoustic emission signals. ASCE J Mater Civil Eng 15(3), 280–286 (2003) 15. F. Kurz, C. Grosse, Stress drop and stress redistribution in concrete quantified over time by the b-value analysis. Struct. Health Monit. 5(1), 69–81 (2006) 16. A. Carpinteri, G. Lacidona, S. Puzzi, From criticality to final collapse: evolution of the “bvalue” from 1.5 to 1.0. Chaos Solitons Fractals 41(2), 843–853 (2009) 17. T. Shiotani, M. Ohtsu, Prediction of slope failure based on AE activity, in Acoustic Emission: Standards and Technology Update, ASTM STP 1353, ed. by S. J. Vahaviolos, (American Society for Testing and Materials, West Conshohocken, PA, 1999), pp. 156–172 18. T. Shiotani, Z. Li, S. Yuyama, M. Ohtsu, Application of the AE improved b-value to quantitative evaluation of fracture process in concrete-materials. J. Acous. Emiss. 19, 118–133 (2001) 19. H. Asuae, T. Shiotani, T. Nishida, K. Watabe, H. Miyata, Applicability of AE tomography for accurate damage evaluation in actual RC bridge deck, in Structural Faults & Repair 2016, CD-ROM (2016) 20. T. Shiotani, K. Ikeda, M. Ohtsu, Detection and evaluation of AE waves due to rock deformation. Constr. Build. Mater. 15(5–6), 235–246 (2001)

Defect Diagnosis of Low-Speed Heavy-Duty Bearings Using Acoustic Emission Guanghai Li, Yang Jiao, and Zhanwen Wu

Abstract A sudden failure of a heavy-duty rolling bearing normally causes high economic loss and has potentially disastrous consequences for the machine operators present. The most established technique for monitoring the integrity of rollingelement bearings is vibration analysis. However, at very slow rotational speeds (less than 3 rpm), monitoring the health of rolling-element bearings is fraught with difficulty. The usefulness of acoustic emission (AE) measurements for the detection of defects in roller bearings has been investigated in this study. Defects were simulated in the roller, inner raceway, and outer raceway of the bearings by the spark erosion method. The AE signals detected on the slewing bearing with no defects (ND), roller element defects (RD), inner raceway defects (IRD), and outer raceway defects (ORD) are done, respectively. Energy-duration correlation of AE signals has been found to be a very good parameter for the detection of bearing with or without defects. Through wavelet analysis of AE signals, the location of defects (RD, IRD, or ORD) can be distinguished. Key words Acoustic emission · Low-speed heavy-duty bearing · Defect diagnosis · Slow rotational speed

1 Introduction Rotary bearings are the core part of a crane and one of the main vulnerable parts. A catastrophic failure of heavy-duty bearings normally causes high economic loss and has potentially disastrous consequences for the machine operators present. Therefore, condition monitoring of such bearings is aimed to detect incipient bearing failure before the consequences of the failure become critical. Therefore, the bearing G. Li (*) · Z. Wu China Special Equipment Inspection and Research Institute, Beijing, China e-mail: [email protected] Y. Jiao Hebei University of Science and Technology, Hebei, China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_23

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needs to be tested periodically or monitored online. Since we cannot measure or visually estimate the bearing failure severity (e.g., raceway failures or rollingelement failures) directly when the bearing is operating, we focused on measuring the physical variables which best characterize the bearing condition. In practice, there are a number of variables that can be monitored directly during the bearing operation. Usually, these can be divided into vibration, acoustic emission (AE), temperature, and wear debris in oil [1]. For high-speed bearing, the role of the information carrier was assigned to vibration (four accelerometers) as the most commonly and efficiently used variable in bearing fault detection [2] and to AE (four sensors), which had been found highly efficient in low-speed bearing applications in earlier investigations [3, 4]. In practice, AE test is a convenient method for periodic or online monitoring for low-speed heavy-duty bearing. Acoustic emission is defined as transient elastic waves generated from a rapid release of strain energy caused by a deformation or damage within or on the surface of a material [5]. In a broad sense, the collision signal produced by roller and raceway is also an AE signal. AEs are defined as the transient elastic waves generated by the interaction of two surfaces in relative motion. The interaction of surface asperities and impingement of the bearing rollers over the seeded defect on the inner race or outer race will generate AEs. In this particular investigation, AE technique is used to detect incipient bearing failure, diagnose defect location, and stop the operation of the system before the consequences of the failure become critical. Acoustic emission signal analysis methods include parametric analysis and wave analysis. These two analysis methods are used to diagnosis whether there are defects in the bearing and to distinguish the location of defects.

2 Experiments 2.1

Experimental Setup

In order to study the application of AE technique in low-speed and heavy-duty bearings, an experimental crane (Fig. 1) was designed. The bearing structure and materials are similar to the actual crane. The laboratory test stand was designed as an independent unit where the actual operational and mounting conditions of heavyduty bearings used in a variety of applications can be simulated. An integrated control system enables modification of parameters such as external loadings, rotational speed, and rotational direction. System response to input parameters is monitored via three wideband piezoelectric AE sensors, which are placed on the outer bearing ring as shown in Fig. 2. By increasing or decreasing the load, we can change the bearing load. By controlling the panel, we can adjust the bearing speed. In addition, the roller, the inner, and the outer raceway defects were made to simulate the actual defects. The experimental plan is illustrated as follows: The AE signal in the slewing bearing with ND and the slewing bearing with RD, IRD, and ORD are acquiring at three different loads and speeds, respectively.

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Fig. 1 Test stand Fig. 2 AE sensor placement and test bearing with and without the artificially defect

2.2

Experimental Procedure

The test stand carries three loads, respectively, no-load (0 t), half-load (4.4 t), and full-loaded (8.8 t). The rotating speed is low speed (1r/min), medium speed (2r/min), and high speed (3r/min). Under the three kinds of loads and three kinds of rotating speed conditions, AE signals were collected and analyzed, respectively, for the slewing bearing with ND, RD, IRD, and ORD. The specific scheme is as follows:

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1. AE detection without defect No-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed, respectively. Half-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed. Full-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed. 2. AE detection of RD No-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed, respectively. Half-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed. Full-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed. 3. AE detection of ORD No-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed, respectively. Half-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed. Full-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed. 4. AE detection of IRD No-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed, respectively. Half-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed. Full-load operation: collect several rounds of AE signals at low speed, medium speed, and high speed.

3 Results and Discussion 3.1

Parametric Analysis of Acoustic Emission Signals

The parameter analysis method is a classic method, which judges the information of acoustic emission sources according to the variation rule of some characteristic parameters of AE signals and the correlation between the parameters, including (1) single-parameter analysis method represented by ring-down count analysis, energy analysis, and amplitude analysis; (2) analysis of distribution diagram; (3) histogram diagram analysis; and (4) correlation diagram analysis. This method is

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Fig. 3 Energy-duration correlation diagram. (a) ND, (b) RD, (c) ORD, and (d) IRD

simple, intuitive, fast in analysis, good in real time, and easy to understand and measure. AE signal parameter correlation analysis was adopted to detect the bearing faults. The energy-duration correlation diagrams have obvious characteristics to distinguish the bearing has defects or not. The defects include RD, IRD, and ORD. The AE signals acquired from the three kinds of bearing were observed. AE parameter correlation analysis was made, respectively. It was discovered that energy-duration correlation diagram has obvious characteristics. It is shown in Fig. 3. From the correlation diagram of defective bearing, it can clearly distinguish two kinds of AE signal that were recorded as A and B (no defects or with defects).

3.2

Spectrum Analysis of Acoustic Emission Signals

In order to accurately collect a full circle of AE signals, the test device adopted the photoelectric external trigger. Figure 4 shows the results of AE signals with or without defects. The signals in areas A and B were analyzed, respectively, as shown in Fig. 3. Figure 4a is the frequency spectrum of acoustic emission signal collected without defect (ND). Its main frequency range is 30–280 kHz.

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Fig. 4 Acoustic emission signal spectrum. (a) ND. (b) RD signal spectrum from area A. (c) RD signal spectrum from area B. (d) ORD signal spectrum from area A. (e) ORD signal spectrum from area B. (f) ORD signal spectrum from area A. (g) ORD signal spectrum from area B

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Figure 4b, c shows the AE signal spectrum of RD. Figure 4b is the spectrum from area A in Figs. 3b and 4 from area B in Fig. 3b. It was found that area A signal spectrum is similar to ND signal spectrum; however, area B signal spectrum frequency range is 30–1100 kHz. It includes the defect signal characteristics. Figure 4d, e shows the AE signal spectrum of ORD. Figure 4d is the spectrum from area A in Figs. 3c and 4e from area B in Fig. 3c. It was found that area A signal spectrum is similar to ND signal spectrum also. Area B signal spectrum frequency range is 30–1200 kHz. It includes the ORD signal characteristics. The same rules apply to IRD. Figure 4f, g shows the AE signal spectrum of IRD. Figure 4f is the spectrum from area A in Figs. 3d and 4g from area B in Fig. 3e. It was found that area A signal spectrum is similar to ND signal spectrum also. Area B signal spectrum frequency range is 30–1050 kHz. It includes the IRD signal characteristics also.

3.3

Wavelet Transform Analysis of Acoustic Emission Signals

For the three kinds of defect location bearings (RD, IRD, and ORD) identification, the wavelet energy spectrum analysis method was applied. Firstly, wavelet denoising was carried out, and then wavelet decomposition and reconstruction were carried out. Finally, the energy distribution coefficients of reconstructed signal components of wavelet coefficients were calculated. The wavelet energy distribution coefficient is defined as the ratio of the energy to the total energy at each wavelet decomposition scale, which characterizes the energy distribution of the AE signal in each frequency band after wavelet decomposition. Such as J-scale wavelet decomposition, the signal is decomposed into J + 1 frequency range components. Due to the difference of AE sources, the distribution of AE signals at each decomposition scale is different from wavelet decomposition. The AE source characteristics can be described by wavelet energy distribution coefficients. Based on the above wavelet energy distribution coefficient method, the three kinds of defective AE signals in the slewing bearing were decomposed by wavelet, and the wavelet coefficients were obtained, as shown in Fig. 5a–c.

Fig. 5 The energy distribution coefficients. (a) RD, (b) ORD, and (c) IRD

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It is shown that the energy of the RD signal is mainly distributed in D4 and D5, and the energy of the ORD is mainly distributed in D4, D5, D6, and D7. The energy of the IRD signal is mainly distributed in D4, D5, and D6. From wave spectrum coefficient distribution, the three kinds of defect signal spectrum distribution exist obvious differences, easy to identify: spectrum coefficient of three kinds of defects on the premise of D5, D4 as the main body in the energy, the energy spectrum of the RD factor D5 layer and D4 accounted for more than 85%, as the main body in the signal energy is absolutely, other layers accounted for only less than 15%, as assistant component. The energy spectrum coefficients of D5 layer and D4 layer with ORD accounted for less than 56%, which is half of the total signal energy, and over 42% of the energy spectrum coefficients of D7 layer and D6 layer, which is also nearly half of the total energy. The D5 and D4 layers with IRD account for nearly 80%, nearly four-fifths of the total energy, and nearly 20% of the energy spectrum coefficient D6 and one-fifth of the total energy.

3.4

Effects of Rotating Speed and Load on Acoustic Emission Test Results

Through experiments, we also study the effect of rotating speed and load on acoustic emission detection results. The experimental results show that the maximum amplitude, cumulative hits, and cumulative energy of acoustic emission signal with defective slewing bearing are higher than those without defect. On the whole, however, the variation law of acoustic emission parameters with the rotating speed is not obvious, which indicates that there is no obvious relationship between the intensity of acoustic emission signal and the speed. Therefore, in the field detection, this is no requirement to the crane rotating speed. That is to say, the rotating speed has no significant influence on the detection result. The cumulative hits and cumulative energy obtained from the three defective bearings (RD, ORD, and IRD) are significantly higher than that obtained from the non-defective bearings for various load conditions, and the hits and energy increase with the increase of load. When the rotating speed is fixed, the energy increases from no load to half load, and the cumulative energy rises faster from half load to full load, indicating that the cumulative hits and energy of acoustic emission signals increase with the increase of load when the bearing is defective. Therefore, in the actual application, a certain amount of load is required to be applied to the acoustic emission detection of crane bearings. From the point of cumulative hits and energy, the larger the load, the more obvious the acoustic emission cumulative parameters caused by defective signals. That has been described in reference [6].

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4 Conclusions The efficiency of the proposed signal analysis methods for the task of recognizing the bearing condition was evaluated on simulated acoustic emission (AE) signals measured on a purpose-built test stand. The fact that the proposed method is able to identify the local bearing defect indicates that AE signals carry sufficient information on the bearing condition and that the proposed method ensures high-reliability bearing fault detection. 1. Compared with the bearing without defects, AE parameter distribution range acquired from defective slewing bearing increases obviously. 2. Using energy-duration correlation diagram, the defect signals can be identified evidently. 3. By signal wavelet analysis, the defect location (RD, IRD, or ORD) can be distinguished using energy distribution coefficients. Acknowledgment This work was supported by the National Key R&D Program of China (Grant No. 2018YFC0809000).

References 1. M. Zvokelj, S. Zupan, I. Prebil, Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis. Mech. Syst. Signal Process. 24, 1049–1067 (2010) 2. N. Tandon, A. Choudhury, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribol. Int. 32, 469–480 (1999) 3. L.M. Rogers, The application of vibration signature analysis and acoustic emission source location to on-line condition monitoring of anti-friction bearings. Tribol. Int. 12, 51–58 (1979) 4. N. Tandon, B.C. Nakra, Defect detection in rolling element bearings by acoustic emission method. J. Acous. Emiss. 9, 25–28 (1990) 5. S. Al-Dossary, R.I.R. Hamzah, D. Mba, Observations of changes in acoustic emission waveform for varying seeded defect sizes in a rolling element bearing. Appl. Acoust. 70, 58–81 (2009) 6. Y. Jiao, G. Li, W. Zhangwen, Experimental study on acoustic emission detection for low speed heavy duty crane slewing bearing. WCAE-2013 Shanghai (ResearchGate, Berlin, 2013)

Investigation on Acoustic Emission Characteristics of Steel Structure of Amusement Device Junjiao Zhang, Gongtian Shen, Zhanwen Wu, Yilin Yuan, and Ran Liu

Abstract Steel structure is the foundation of the amusement device. The health status of the steel structure is crucial to the safe operation of amusement device. In order to realize the overall monitoring of the steel structure, the application of acoustic emission technique for online testing on a disco turntable has been studied. The attenuation regularity of AE signals and the sound velocity of the steel structure were tested. AE signals of the steel structure were collected during the normal operation of the disco turntable. The AE characteristics including AE parameter features, spectrum features, and location features were analyzed. The results of this study indicated that the AE technique has great potential for the condition monitoring and safety evaluation of the steel structure of amusement device. Key words Steel structure · AE characteristics · Amusement device

1 Introduction In recent years, with the growth of the national economy and the improvement of people’s living standards, the amusement industry has developed rapidly. By the end of 2017, the quantity of large-scale amusement devices has come to 24,200 in China, an increase of 8.5% over 2016. Also, the number of large fairground or theme park is more than 400. There are more than hundreds of millions of times people have experienced the amusement device each year. Steel structure is the foundation and the supporter for the amusement device. The health status of the steel structure is crucial to the safe operation of amusement device. In the running process of amusement device, the acceleration is changing along with rise, fall, torsion, and other kinds of motion. Due to the diversity of movements and load of amusement device, active defects of the steel structure will appear in the process of the device operation. The

J. Zhang · G. Shen (*) · Z. Wu · Y. Yuan · R. Liu China Special Equipment Inspection and Research Institute, Beijing, China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_24

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defects of the track will lead to accidents and directly threaten the safety of passengers’ life. At present, thickness measurement is the main method for inspection of steel structure. Also, magnetic particle testing on the surface will be conducted for sampling parts of steel structure. This method can detect in a small range with low speed and low efficiency and also does not have the ability to discover early damage. Acoustic emission technology can be used for online detection and has potential of early damage forecasting. Therefore, the purpose of this study was to explore the feasibility of applying acoustic emission technology to detect and evaluate the structural integrity of the amusement device. There are a large number of studies showing that using acoustic emission technology for the online detection and safety evaluation of steel structures of railway track, steel bridge, and crane beam has good effect [1–3]. Most material of the steel structure of amusement device is Q345 carbon steel which acoustic emission characteristics have been obtained from the research results of pressure vessel [4]. For the steel structure of amusement device, our research team has carried out acoustic emission testing on the roller coaster track and achieved good results [5]. As a consequence, investigation on acoustic emission characteristics of signals acquired in the running process of the amusement device establishes the foundation for the AE detection of the track. In this paper, acoustic emission online testing of the steel structure of disco turntable was realized in a fairground. The attenuation characteristics of AE signals and the sound velocity of the steel structure were obtained. AE signals were collected when the turntable was running. The AE characteristics of the signals including AE parameter features, spectrum features, and location features were obtained. The results of this study provide a new method and basic data for the overall rapid detection of the steel structure of amusement device.

2 Testing Object and Equipment Taking a disco turntable as the testing object, AE online testing of the U-shaped steel structure was carried out in a large fairground in China. The disco turntable is composed of U-shaped track, turntable, electric control system, and other parts. The turntable swings left and right along the U-shaped track during the operation of the device. The maximum swing angle of one side is 55 and the radius of rotation is 3.3 m. The turntable can rotate in a clockwise or counterclockwise direction while swinging with the maximum speed of 14 rpm. The swing of the turntable uses a friction drive and the rotation uses a gear drive. The U-shaped track is produced by Q345 carbon steel structure which consists of track surface, long pillars, short pillars, and a base. The adjacent parts of the track were connected by bolts. The top height of the U-shaped track is 9.23 m and the total length is 25 m. The test objects are the track surface and the pillars, both of which are I-beam steel structures with the wall thickness of 11 mm. The testing system consists of the testing disco turntable, an AMSY-6 digital multichannel acoustic emission instrument, sensors, computer (signal processing software), etc. For the AE data acquisition, the broadband sensors of model VS900-

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Fig. 1 Testing object

M whose bandwidths were between 100 and 900 kHz and VS45-H whose bandwidths were between 20 and 450 kHz were used. Also, the resonant sensors of model VS150-RIC with peak response frequency of 150 kHz were employed, with an operating frequency range of 100–450 kHz. The gain of the built-in amplifier in sensors is 34 dB. The appearance of the testing disco turntable is shown in Fig. 1.

3 AE Attenuation Characteristics of the U-Shaped Track In order to confirm the effective monitoring range of the sensors, AE attenuation characteristics of the track were studied first. For easy operation, the attenuation test was conducted on the long pillar by 2H lead broken method. Sensor VS150-RIC was installed on the top of “-” side of steel I-beam structure with total length of 4 m. AE signals of “-” side and “|” side were obtained separately during the attenuation tests. Test results show that the amplitude of AE signals of “-” side decreased quickly within 1.5 m and then fluctuated at slow attenuation speed. In the distance of 4 m from the sensor, the amplitude of AE signals attenuated within 20 dB. On the “|” side, the average amplitude of AE signals was 6 dB lower than the signal amplitude of “-” side. The amplitude of AE signals of “|” side decreased quickly within 1 m and then fluctuated at slow attenuation speed. In the distance of 4 m from the sensor, the amplitude of AE signals attenuated within 18 dB. The acoustic emission attenuation curve was shown in Fig. 2.

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Fig. 2 Attenuation curve of the U-shaped track

4 Sound Velocity Test of the U-Shaped Track 4.1

Sensors Arrangement

The operation of the disco turntable is a combination of two forms of motion, swing and rotation. The turntable swings left and right along the U-shaped track while rotating. At the bottom of the U-shaped track, the turntable swings at the maximum speed, and the swing speed drops to zero when it is running to the top. The middle part of the U-shaped track was selected, and eight sensors were arranged symmetrically along the two I-beam structures for AE testing. Figure 3 shows the sensors arrangement on the testing track. By means of magnetic adsorption, sensors were mounted on the “|” inside of the U-shaped track. Sensors VS150-RIC (No. 2#, No. 4#–8#) were used for discovering the parameter features and location features of AE signals. In addition to participate in the AE location test, sensors VS45-H (No. 1#) and VS900-M (No. 3#) were selected to find the spectrum characteristics of AE signals.

4.2

Testing Results

Since a number of box-shaped steel structures are welded between the two U-shaped steel tracks, also the track is connected with the pillars and the base by bolts, which makes the U-shaped tracks more complicated. In order to locate the AE signals of the track accurately, we test the sound velocity of the sensors on the two U-shaped

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Fig. 3 Sensors arrangement

tracks, respectively. The channels connected with sensors were inspired in turn by using self-calibration function of the instrument. According to the arrival time and wave propagation distance of the AE signals, the sound velocities of the two tracks were calculated. The calculated values of sound velocities were revised according to the location results of self-calibration. Then the real sound velocities were obtained. The results show that the sound velocities of the two tracks are same, which value is 4800 m/s.

5 AE Characteristics of the U-Shaped Track Start the disco turntable running at normal condition. When the turntable is moved to the highest position and begins to descend, AE signals were collected continuously when the turntable repeatedly passes the test track. The test repeated two times in the same condition. Set the threshold 40 dB to remove background noise on site. The sampling rate is set to 5 MHz and the sample time is 1600 μs.

5.1

Parameter Characteristics

Comparing the AE parameter courses of the two tests, we found that all the AE signals have the same parameter characteristics. Take the first test results of sensor VS150-RIC as an example; the AE parameters changed randomly with time during the operation of the turntable. See in Fig. 4. From the correlation graph of the energy with duration time, it can be seen that there is only one kind of signal of the track. By observing the parameter distribution graphs of the AE signals, the distribution features were obtained. Table 1 shows the parameter distribution and concentration ranges.

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Fig. 4 AE parameter courses. (a) Amplitude-time. (b) RMS-time. (c) Energy-time. (d) Energyduration time Table 1 Parameter distribution of AE signals AE parameter Amp/dB Energy/eu Counts

5.2

Distribution 40–56 1–5000 1–2000

Concentration 40–43 1–40 1–30

AE parameter Dur/μs Rise time/μs RMS/μV

Distribution 1–8  104 1–2  104 6–29

Concentration 1–450 1–500 16–26

Waveform and Spectrum Characteristics

For the AE signals of the track during the running process of the disco turntable, the waveform and spectrum analysis were carried out. Figure 5 shows the waveform and spectrums of AE signals from the broadband sensors VS45-H and VS900-M and also the resonant sensor VS150-RIC. The AE signals of VS45-H have the frequency characteristics which covers the range of 20–50 kHz with one obvious peak at 25 kHz. For the AE signals of sensor VS900-M, the frequency distributes in the range of 30–180 kHz, and the energy is concentrated in 50–100 kHz. The AE signals of VS150-RIC have the similar frequency characteristics which covers a wide range of 20–280 kHz with one obvious peak at 30 kHz, also a little peak around 150 kHz.

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(a)

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(c) Fig. 5 Waveform and spectrums of AE signals. (a) Sensor VS45-H (No. 1#). (b) Sensor VS900-M (No. 3#). (c) Sensor VS150-RIC (No. 7#)

5.3

Location Characteristics

Location analysis has been conducted on the AE signals of the two tests. Figure 6 shows the linear location results of the test tracks of the two tests. The results show that a number of location events appeared during the operation of the turntable. For each test, the location group 1#–3#–5#–7# obtained more AE location events which

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(a)

(b)

Fig. 6 AE linear location of the test tracks. (a) First test. (b) Second test

Table 2 AE parameter distribution of location signals AE parameter Amp/dB Energy/eu Counts

Distribution 40–50 3–107 1–74

Concentration 40–44 3–28 1–15

AE parameter Dur/μs Rise time/μs RMS/μV

Distribution 1–2230 1–384 5–24

Concentration 1–185 1–46 8–22

concentrated between the sensors 3# and 5#; also few location events appeared between the sensors 1# and 3#. Very few AE location events were obtained in the location group 2#–4#–6#–8# and the positions were different. Comparing the results of the two location groups, it was found that the position of the location events in the group 2#–4#–6#–8# also has location events generated in the group 1#–3#–5#–7#. Since there were two broadband sensors in the group 1#–3#–5#–7#, more AE signals were acquired in the test which result in more location events. The speed of the turntable was highest when it was running pass the bottom of the U-shaped track. The steel structure was pressed and a large number of location events generated between sensors 3# and 5#. The parameter distribution characteristics of the AE location signals were analyzed. Table 2 shows the parameter distribution ranges of the AE location signals. It can be concluded that the AE location signals have low amplitude and energy, short duration time, and few counts. All the location signals have the same waveform and spectrum characteristics by observing and comparing analysis. Figure 7 shows the waveform and spectrum of the typical location signal from sensor VS150-RIC. The frequency of the location signals mainly distribute in the range of 30–100 kHz with an obvious peak at 70 kHz. The characteristic frequency of the AE location signals mainly concentrates in the low-frequency domain.

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Fig. 7 Waveform and spectrum of AE location signal Table 3 AE parameter distribution of crack propagation signals (Q345) AE parameter Amp/dB Energy/eu Counts Dur/μs Rise time /μs

Hits signals Distribution 38–100 1–6  104 1–2  104 1–2.5  105 1–3000

Concentration 40–70 1–1  103 1–2  104 1–300 1–200

Location signals Distribution 38–100 12–6  104 1–2  104 1–2.5  105 1–1100

Concentration 40–70 10–100 1–200 30–300 20–100

6 Analysis The research group of Dr. G.T. Shen has investigated the acoustic emission characteristics of the Q345 carbon steel during the process of crack propagation on the pressure vessel. The AE parameter distribution ranges of the hits and location signals of the crack propagation were listed in Table 3 [4]. Comparing with the AE parameter distribution features of the disco turntable track in Tables 1 and 2, it can be found that the maximum values of all AE parameters of the crack propagation signals are obviously higher than the signals of track during the operation of disco turntable. In addition, source intensity division values of the Q345 carbon steel using AE parameter of amplitude were provided. AE signals with amplitudes less than 60 dB are divided into low-intensity signals. The amplitude of all AE signals of the tracks collected in the test is less than 60 dB and is concentrated below 45 dB. Based on the above analysis, the AE signals of the track are the noise during the normal operation of disco turntable. If there is an active defect like crack on the track, a lot of location events will appear in the position of the crack with high amplitude and energy. The AE signals can indicate the location and severity of the defect which can be obviously distinguished from the running noise.

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7 Conclusions AE online detection was carried out on the track of a disco turntable in fairground. The attenuation regularity of AE signals and the sound velocity of track were obtained. The analysis of the AE signals during the turntable operation revealed the following conclusions: 1. AE signals of the track are appearing during the operation of the disco turntable. AE events are located on the testing tracks by using the AE linear location method. The AE location signals are low-frequency signals with low amplitude and energy, short duration time, and few counts. 2. The AE signals of the track are mainly running noise which spectrum energy concentrates below 100 kHz. 3. The amplitudes of the AE signals are less than 60 dB. According to the AE characteristic of the track material, the AE technology can be used for monitoring the active defects like crack propagation on the track. 4. The AE technique could be used as a practicable means for the online detection and safety evaluation of the structural integrity of disco turntable track. Combined with the author’s previous research on AE testing of roller coaster track, it can be concluded that the application of AE technology to the safety evaluation of the steel structure of amusement device has great advantages. Acknowledgment This study was financially supported by the national key research and development plan project 2017YFC0805704-5.

References 1. K. Bruzelius, D. Mba, An initial investigation on the potential applicability of Acoustic Emission to rail track fault detection. NDT E Int 37(7), 507–516 (2004) 2. G.T. Shen, Z.W. Wu, Investigation on acoustic emission source of bridge crane. Insight 52(3), 144–148 (2010) 3. O. Yapara, P.K. Basua, P. Volgyesib, A. Ledeczib, Structural health monitoring of bridges with piezoelectric AE sensors. Eng. Fail. Anal. 56, 150–169 (2015) 4. G.T. Shen, Acoustic emission technology and application (Science Press, Beijing, 2015), pp. 152–159 5. J.J. Zhang, G.T. Shen, Z.W. Wu, Y.L. Yuan, Investigation on acoustic emission characteristics of roller coaster track, in Progress in Acoustic Emission XVIII, Proceedings of the 23rd International Acoustic Emission Symposium, pp. 419–424 (2016)

Case Studies on Tank Bottom In-Service Acoustic Emission Testing and Its Verifications Yewei Kang, Zhenghong Guo, Yi Zhang, and Huatian Xu

Abstract After years of research and practice, the PetroChina Pipeline R&D Center has developed an effective method for tank bottom in-service acoustic emission testing, which has been popularized and applied in PetroChina Pipeline Company. In 2013 a pipeline company planned to introduce acoustic emission (AE) technology, but there were some doubts about the effectiveness of this technology, so it invited our center to test five tanks with AE before their follow-up internal inspections and to verify the validity of the AE test by comparing AE results with the follow-up inspection results. The results show that the AE test results of four tanks are in line with the follow-up internal inspections, and one AE test result is severely deviated with the real tank bottom status. In view of the deviation, the reasons are analyzed from four aspects, i.e., the lack of tank information, the characteristics of found defects, the review of original AE data, and the property of acoustic emission technology, which are accepted by the tanker owner. Ultimately, the company plans to apply acoustic emission technology to their tank maintenance process. This paper describes in detail the acoustic emission detection and verification process in order to give some references for the industry counterparts, while promoting the development of the AE technology. Key words Tank bottom · Acoustic emission · In-service inspection · Result verification

Y. Kang (*) · Z. Guo · H. Xu PetroChina Pipeline R&D Center, Langfang, Hebei, China e-mail: [email protected] Y. Zhang Collective Project Management Department of PetroChina Pipeline Company, Langfang, Hebei, China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_25

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1 Introduction The aboveground vertical storage tanks are widely used in petrochemical industry for storing a variety of chemical materials and products whose maintenance and testing are necessary procedures for their safeties. When the tank is normally operating, the tank bottom is not easy to touch; its detection is difficult than the tests of the tank wall and some accessories, unless the tank is shut down and opened which will cost a lot of money and time. From the corrosion point of view, the tank bottom is also the most severe part of the tank. So some in-service inspection technologies emerge because of the demand, which don’t need the tank to be shut down and can be applied when the tank is full of oil. The acoustic emission (AE) technology is one of them, which was first used to assess the corrosion of tank bottom in the late 1980s [1]. From then on, it attracts great attention because it can do in-service inspection. After years of research and practice, the PetroChina Pipeline R&D Center has developed an effective method for tank bottom in-service AE testing, which has been popularized and applied in PetroChina Pipeline Company [2]. In 2013, a domestic pipeline company planned to introduce acoustic emission (AE) technology, but there were some doubts about the effectiveness of the technology. So it invited our center to test five tanks with AE before their followup internal inspections and to verify the reliability of the AE test by comparing AE results with the follow-up inspection results.

2 Descriptions of Oil Tanks The five tanks whose detailed information shown in Table 1 are distributed in four oil stations; three of them store crude oil, and two store refined oil. As can be seen from Table 1, the 1#, 2#, and 5# tanks have not yet been tested, and the 3# and 4# tanks had been inspected in 2006 and 2002, respectively. According to SY/T 5921 code, i.e., practice for operating, maintenance, and repair Table 1 Tank information No. 1# 2# 3# 4# 5#

Tank type Internal floating roof Internal floating roof External floating roof External floating roof External floating roof

Diameter, m 40

Year of first operation 2005

Medium Gasoline

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28.5

1992

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60

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of vertical cylindrical welded steel oil tank, the internal inspection cycle of a tank is generally 5–7 years except the new tank whose first internal inspection should not exceed 10 years. The operation period of 1#, 2#, and 5# tanks is less than 10 years; its operation can continue. The 3# tank has run for 7 years; it’s time to do internal inspection. The service periods of 4# tank exceed 7 years; it should be overhauled as soon as possible.

3 AE Testing 3.1

Procedure of AE Testing

AE testing mainly relies on acoustic emission phenomenon of materials. The active defects in the tank bottom will produce acoustic emission phenomenon because of the load exerted by the liquid in the tank. When the AE sounds from the tank bottom are received by some sensors attached on the outer wall of the tank and then recorded and analyzed by special hardware and software, the condition of the tank bottom can be assessed. The tank bottom acoustic emission testing is a passive detection technology, relying on AE sound generated by active defects which are generally weak, vulnerable to external environment interference, so the reliable data collection is extremely important. Our center has developed an effective AE testing which includes the following steps [3]: 1. Fill the tank to greater than 80% capacity; and all agitators, heaters, and pumps are turned off; and keep the tank at rest for a period of 12–24 h. 2. Mount sensors around the perimeter of the tank about 0.5–1 m distance to the tank bottom, and connect them to an AE system. Guard sensors can also be employed in order to filter background noise being generated in the upper section of the tank. 3. Calibrate and check the system; make sure that test conditions are all right. 4. Record AE data for at least 10 h and repeat at least two times. 5. Pack up equipment; analyze the recorded data.

3.2

AE Grade and Testing Results

As to our method, the less interfered data segment is selected and analyzed based on the grade of AE event cluster and the number of registered hits per channel. The AE event cluster mainly indicates the “potential leak sources” on the tank floor occurring in nearly the same location. These clusters are then further analyzed to assess the magnitude of the source, level I–IV. The number of AE hits per hour represents the corrosion activity, level I–V. The principle of cluster division and its meaning are as follows:

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Level I: no clusters, no leakage on the tank bottom. Level II: 1–2 clusters each with 5–10 events, uncertain leakage on the tank bottom. Level III: 3–4 clusters each with 11–20 events, possible minor leakage on the tank bottom. Level IV: 4+ cluster each with more than 20 events, distinct leakage on the tank bottom. The classification of each hit activity is as follows: Level I: N < m. Level II: m  N < 2m. Level III: 2m  N < 20m. Level IV: 20m  N < 200m. Level V: N  200m. Here N is the number of AE hits per hour. The value of m depends on the tank conditions, such as its structure, its inclusion, and its dimension, and usually gained from experiments. The overall severity of corrosion at the tank bottom is divided into five grades, grades A–E. The grading rule of AE and the meanings of each grade are shown in Tables 2 and 3, respectively. The tank bottom in-service inspections of the five tanks were completed in September 2013, and the final results are shown in Table 4.

Table 2 Grading rule of AE inspection

Cluster level I II III IV

Hits activity I II A B B C C D D E

III B C D E

IV C C E E

V D D E E

Table 3 Meaning of AE grade and associated recommendations Grade A B C D E

Degree of active defects Very minor Minor Intermediate Severe Highly severe

Table 4 AE testing results of five tanks

Tank number 1# 2# 4# 3# 5#

Recommendations Retest in 5 years Retest in 3 years Retest in 2 years Internal inspection within 1 year Immediately internal inspection

AE grade B D C B A

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4 Verification Based on the internal inspection results provided by the tank owner, it is found that the AE grades of the 1#, 2#, 4#, and 5# tanks are accurate and the one of the 3# tank, which is optimistic, isn’t consistent with the result of internal inspection. The specific comparisons are as follows. The internal inspection of 1# tank was completed in June 2015, and the test results show that there are 162 corrosion pits in the tank bottom whose depths are less than 5% of the original thickness of tank bottom, which is moderate corrosion but within the requirement of maintenance standard. The AE grade of this tank is B level indicating that a small amount of corrosion exists and retest should be done in 3 years. Obviously the acoustic emission evaluation result is consistent with the internal inspection result. The internal inspection of 2# tank was completed in August 2015, and the test results show that there are 134 pit corrosions with depth of greater than or equal to 20% of the wall thickness distributed on the center plate of the tank bottom and one perforation is in the annular plate. The AE grade of 2# tank is D level, indicating tank bottom corrosion degree is severe. The tank is recommended to carry out internal inspection within 1 year. It can be seen that the evaluation of AE testing is accurate. From Table 1 it can be found that the internal media and construction year of 1# tank and 2# tank are the same, but their corrosion conditions are obviously different. Due to the lack of detailed information about two tanks, we don’t know the exact reasons. We guess that one important reason is that their operating manners are different, so the accumulated sludge on the tank floor is different, which induced significant difference of corrosion. The internal inspection of 3# tank was completed in December 2013, and the test results show that there is severe corrosion on the tank bottom; 74 corrosion perforations were found. However, the AE grade of 3# tank is B which represents minor corrosion severity. Obviously, the AE result is inconsistent with the real situation of tank bottom. The cause leading to such inconsistence will be analyzed next section. The tank bottom plate of 4# tank consists of annular plate with thickness 12 mm and center plate with thickness 8 mm. According to SY/T 5921, for the annular plate, the qualified criteria of average plate thickness are less than 15% of the original thickness, i.e., 1.8 mm, and for the center plate, the average thinning is less than 20% of the original thickness, i.e., 1.6 mm. The internal inspection of 4# tank was completed in December 2014. The test results show that there are two pits on the annular plate with the depth 2 mm, which exceed the qualified criteria, and there are 11 pits on the center plate with the depth between 2 mm and 2.3 mm. In general the corrosion severity of the tank bottom of 4# tank is intermediate. The corresponding AE result is C level, also an indication of moderate corrosion severity, whose maintenance recommendation is retest with AE technology within 2 years. Of course some suitable measure should be taken during the tank operation. Obviously the acoustic emission evaluation is consistent with the internal inspection result. The internal inspection of 5# tank was completed in August 2015; the test results indicate that the corrosion depth of the whole tank bottom is less than 20% of

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original plate thickness, which was tested by magnetic flux leakage detection technology. And the corrosion depth of the tank bottom tested with the ultrasonic gauge also falls within the qualified criteria of SY/T 5921. The result of in-service AE testing of this tank is A grade; it means only very minor corrosion has happened and the tank can be retested within 5 years. It is obvious that the AE evaluation is consistent with the actual corrosion status of the tank bottom.

5 Reason for Difference in Testing Results At the requirement of the tank owner, the reasons for the difference between the acoustic emission detection and the internal inspection of 3# tank are analyzed. Here the reasons of difference are described mainly from the point of views of the lack of tank information, the characteristics of found defects, the reanalysis of original AE data, and the property of acoustic emission technology.

5.1

Lack of Tank Information

The 3# tank was put into operation in 1992, but its first internal inspection was done until 2006 after running for 14 years. However the tanker owner did not provide us the internal inspection result when testing the tank bottom with AE. We don’t know how serious the corrosion on the tank bottom and whether or not some repairs have been carried out. The lack of related information is detrimental to the accurate evaluation of AE grade.

5.2

Characteristics of Found Defects

Many corrosion perforations exist in the tank bottom, but during the period of tank operation and internal inspection, no leakage sign is observed, indicating that the sludge layer has played a role in blocking the liquid leakage. In addition, some so-called perforations as indicated in Fig. 1 are not through holes; there is still a certain thickness, but very close to through hole. Based on the above statement, two questions are raised: (1) Are these corrosive perforations only developed after the last internal inspection? (2) What corrosion level were those perforations at the time of 2006? As to the first question, the possibility is unlikely. If the corrosion severity of tank bottom operating 7 years (from 2006 to 2013) is so serious, it should be much more serious for running 14 years (from 1992 to 2006) which will make the tank owner impressed. Therefore, we infer that these defects are likely to be developed on the basis of the ones formed in 2006. The answer to the second question is unknown. Because acoustic emission is a passive detection technology, which can only detect

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Fig. 1 Pits close to through hole highlighted by the green circle

“active” defects, i.e., the developing defects. In 2006 the 3# tank was opened and carried out internal inspection, which would result in the reset of the states of some unrepaired corrosions on the tank bottom. For AE technology only the corrosions developed from 2006 to 2013 can be detected and evaluated, the corrosions formed from 1992 to 2006 can’t be found due to the state reset [4]. Therefore, from the point of view of AE technology, the characteristics of many actual perforations are not prominent, which will induce wrong assessment.

5.3

Review of Original AE Data

Figure 2 shows the raw location results of AE events of the 3# tank. It can be seen that during the 12-h continuous data collection, there are many areas where the concentration of AE events is high, as shown in the green circles. This indicates that original data is credible and the AE technology has a certain degree of science. However, it is difficult to determine whether these concentrated areas correspond to the location of the corrosive perforations or the deeper pits. With our past experience, it is impossible to imagine that there will be 74 perforations in the tank bottom only operating for 7 years. The postmortem analyses illustrate that many factors lead us to filter out some useful information when the data were analyzed and to achieve the B grade.

5.4

Properties of AE Technology

The tank bottom in-service acoustic emission testing is affected by many factors [5]. The accuracy of the evaluation results is statistically meaningful and not 100% accurate. For example, the Physical Acoustic Company has drawn comparison

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Fig. 2 Original location graph of AE events monitoring for 12 h

between the AE grades and the follow-up internal inspection results with 157 tanks, which show that about 10% of tanks tested with AE and evaluated as B grade actually have serious damage. For the E grade tanks, 60% of them have extensive damage and require major repairs or a new floor [1]. All in all, AE technology is accurate for tanks with minor corrosion or severe corrosion. For the leak detection, a company had examined 345 tanks using the acoustic emission leak detection method. Twenty-one of the tanks were indicated to be leaking, and 19 of the 21 were internally inspected. Of those 19 suspects, 16 actually had leaks. This gives a probability of detection of 0.84 and a probability of false alarm of 0.16 [6]. Based on the facts that the last internal inspection result is lacking and the original data is valid and the AE testing can’t detect the formed defects if their states are reset, we think that the lack of tank information and the reset of corrosion state are important factors to cause the optimistic result of AE data analysis, although other factors also play some roles, but it is difficult to determine. After the above analysis, the tanker owners recognized the reasons for the difference.

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6 Conclusion Five tanks were tested with the AE testing technology provided by PetroChina Pipeline R&D Center in 2013. After AE testing the internal inspection of these tanks were carried out from 2013 to 2015. By comparing the AE results with internal inspection results, it is found that four tanks’ AE grades are accurate and one tank’s AE result is different with practical tank bottom condition. The reasons causing the difference were analyzed from four aspects, i.e., the lack of storage tank information, the characteristics of defects found in tank bottom, the review of original AE data, and the property of AE technology. Considering the lack of last internal inspection information and the original data is valid and the AE testing can’t detect the formed defects if their states are reset, we draw a conclusion that the lack of tank information and the reset of corrosion states are important affect factors, which were recognized by the tank owner. At last the role and value of AE technology in the tank maintenance process are demonstrated through these practices, which promote a profound understanding of AE for the tank owner and are beneficial for the application of it.

References 1. P.T. Cole, S.N. Gautrey, Development history of the Tankpac™ AE. Tank bottom Corrosion Test. NDT Net 7(9), 1–12 (2002) 2. Y.W. Kang, M.C. Lin, M. Xiong, D.J. Tan, W.B. Wang, A combined method for analysis of the acoustic emission signals from aboveground storage tank bottom, in China International Oil and Gas Pipeline Conference, Langfang, November 2009 3. H. Xu, X. Liu, Z. Guo, Y.W. Kang, H.Y. Chen, Comparison Between Acoustic Emission In-Service Inspection and Nondestructive Testing on Aboveground Storage Tank Floors. Springer Proceedings in Physics, vol 158 (Springer, New York, NY, 2015) 4. D. Papasalouros, K. Bollas, D. Kourousis, A. Anastasopoulos, Acoustic emission tank bottom testing a study on the data-base of tests and follow-up inspections, in 31st Conference of the European Working Group on Acoustic Emission (EWGAE), Dresden, September 2014 5. Y.W. Kang, W. B. Wang, M. C. Lin, Z. Ren, J.P. Zhu, J. Xu, Factors influencing the acoustic emission inspection of above ground storage tank floor and its countermeasures, in Proceedings of World Conference on Acoustic Emission, Beijing, November 2009 6. E. Myers Philip, Preventing leaks in large aboveground storage tanks, in PetrolPlaza, May 1999

Corrosion Degree Evaluation and Leakage Judgment of Vertical Storage Tanks by AE Test Yanting Xu, Yadong Wang, and Weigang Zhang

Abstract Acoustic emission (AE) testing has gradually been applied to the online inspection and safety evaluation of the vertical storage tank bottom, but the evaluation is mainly qualitative, and it is difficult to evaluate the nature of the sound source (corrosion or leakage), so its application and effect are limited. A preliminary severity evaluation of corrosion defects was made, and the nature of acoustic sources was screened in this paper with confirmed AE testing cases of tank bottoms as reference samples, and the concept of similar evaluation method based on event rate and graphical features was put forward. With more experience and further research in this field and in the future, a simple and effective method for corrosion degree evaluation and defect property identification will be developed. Key words Vertical tank bottom · Corrosion degree evaluation · Leakage determination · AE online testing · Event rate · Similar evaluation method

1 Introduction AE testing has become an important means for online inspection and safety evaluation of atmospheric pressure vertical tanks. Especially for large storage tanks and hazardous chemical storage tanks, the shutdown inspection needs enormous cost for the replacement and cleaning and environmental treatment of tanks, and it has a great influence on production because the inspection time is usually more than 1 month. Y. Xu (*) Zhejiang Provincial Special Equipment Inspection and Research Institute, Hangzhou, China Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province, Hangzhou, China Y. Wang Zhejiang Provincial Special Equipment Inspection and Research Institute, Hangzhou, China W. Zhang College of Quality and Safety Engineering, China Jiliang University, Hangzhou, China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_26

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AE technology is relatively mature, and its application is also very convenient. In fact, AE test has played an important role in the online inspection and evaluation of tanks. In China, large vertical atmospheric tanks have not classified as special equipment, so government regulators will not normally require regular inspection before then. But with the occurrence of all kinds of production safety accidents in recent years, the government has now strengthened the safety supervision of such tanks and required inspection reports when issuing new licenses. Due to the previous backlog of untested tanks and the considerations in production and environmental protection, it is impossible to implement shutdown test for all these tanks in a short term. Therefore, AE online testing and evaluation technology becomes the first choice, which not only can save a great deal of expenses for the replacement and cleaning and environmental protection of tanks, but also the detection time is greatly shortened, and it has little influence on the normal production. AE technique has been mainly limited to the qualitative safety evaluation of storage tanks. The identification of the effective acoustic sources and the discrimination of the defect properties are inadequate, and the reliability of the detection and evaluation results is not high enough. At the same time, many domestic and international AE standards only provide vague descriptions of some specific evaluation methods; even the basic criterion data is not supplied. For example, in the JB T 10764-2007 “nondestructive acoustic emission testing and evaluation of atmospheric pressure metal storage tanks,” the reference values of n and m used for acoustic source evaluation are not provided [1]. In fact, these reference values are difficult to obtain. Therefore, the technology cannot fully meet the needs of the owners for the online inspection and safety evaluation of the tanks. Therefore, it is urgent to further improve the reliability of AE detection and to study the corrosion degree evaluation of acoustic sources on the basis of the traditional AE detection. For this purpose, the similar evaluation method of tank safety based on AE “template” cases is proposed in this paper. This method does not need complex analysis as AE advanced analysis software (neural networks or wavelet methods) which is difficult to apply and rarely adopted. With the constant enrichment of various tank template cases, AE detection and evaluation of tanks will become a relatively simple work, and AE technology will be rapidly popularized and applied.

2 Similar Evaluation Method for Tank AE Testing In medicine, there is a similar treatment called homeopathic. The similar evaluation method of AE testing includes similar tanks, similar detection method and data processing, similar event rate calculation, observation of acoustic source distribution, and similar evaluation based on template cases. The definition of the similar evaluation method of tank AE test is that similar tanks inspected by the similar test method should be analyzed and evaluated according to the analyzing methods and the reference criterion provided by the

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model cases. In order to simplify the evaluation procedure, only the event rate and the distribution characteristics of the acoustic source localization group are used as the main evaluation parameters.

2.1

Definition of Similar Tanks

Similar tanks are defined as those tanks with similar material (such as carbon steel or stainless steel), structure (such as a vault or floating roof, internal structure), diameter and bottom thickness, medium property, and working conditions to the model cases.

2.2

Similar Detection Method and Data Processing

The setting of detection parameters, the detection process, the loading mode, and speed should not only meet the relevant AE standards but also should be as close as possible to the model case. The wave velocity should be selected according to the medium or measured in field. In order to facilitate data analysis and observation, the resolution of the planar location display is uniformly defined as 200, and the bin number of the 3D location display is recommended not to exceed 0.5 m/bin, as follows: Tank diameter Φ
6 m, 20 bins. 6 m < Φ
10 m, 40 bins. 10 m < Φ
20 m, 50 bins. 20 m < Φ
40 m, 100 bins. Φ > 40 m, 200 bins. When a larger bin number is selected, the acoustic source locations in the 3D positioning map may be observed more clearly, but the adjacent acoustic sources may be merged into a sound source.

2.3

Definition of Event Rate

The event rate ε is defined as the total number of events (∑E) per hour and per square meter in a positioning group or a region involved, i.e., ε ¼ ∑E/T/S. T refers to the detection time or hold time in hours in the highest detection level, and S is the area in square meters of the source region being analyzed. For the evaluation of the maximum corrosion depth, the most centralized events in an acoustic source location group can be counted for the event rate calculation when the group has a larger area (above 25 m2), and total events in the whole area should be counted for an acoustic source group with a smaller area (less than 4 m2).

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Discrimination of Acoustic Source Shape

The distribution of the acoustic source locations is observed. Three-dimensional locations can be divided into the uniform distribution in the whole plate, positioning group in a larger area, and centralized positioning in a smaller area. The planar locations can be divided into the uniform distribution in the whole plate, dense point locations in a larger area, and dense point locations in a smaller area. In the 3D locations, it is easier to find concentrated sound sources, which often represent severe defect areas. In the planar locations, the “radiate” shape is often associated with fluid flow.

2.5

Similar Evaluation

The similar evaluation is made by comparing the event rate of a similar tank with that of the template tank and by the observation of the positioning shape. The evaluation results can be divided into the overall corrosion, large area corrosion, local pitting corrosion, or lake-like corrosion. The maximum corrosion depth can be estimated, and the leakage position can be located if a corrosion perforation is likely to be considered.

3 Template Cases and Analysis These two tank cases cited in this paper are from the tanks inspected by the author, and the results of AE test were verified by cutting plate and/or magnetic flux leakage test (MFL) [2].

3.1

Case 1: A 5000 m3 Tank with a Corrosion Perforation (G503)

Tank parameters: atmospheric temperature; atmospheric pressure; size, φ22000  13,600 mm; volume, 5000 m3; floor thickness, 10/8 mm; material, Q235; age, 26-year-old; operating medium, gasoline. Test condition: PAC DiSP AE workstation, 12 integrated sensors (R3I, 30–60 kHz) were evenly distributed along the tank wall circumference 200 mm from the base plate; operating/testing liquid level is 8 m or so. The tank was suspected of leaking by the owner, so the level was not raised to the desired level, and it was monitored for only 1 h.

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Source locations of the tank bottom are shown in Fig. 1. Only one significant source group was found in a large area of the central bottom. It is easy to see from the planar location that the distribution of the locating points is “radiate” with a shape flowing in several directions. Since the sound source group is located in a larger area (about 50 m2), the event rates are calculated for the higher and the highest peak regions of the whole group, respectively, in order to estimate the maximum corrosion depth. The results are shown in Table 1. After opening the tank, a corrosion perforation (about φ10 mm) was found in about 1 meter to the tank floor center. The owner replaced the entire tank floor. A great deal of severe corrosion was found on the lower floor surface located in the acoustic source group, with the depths of about from 40% to 80% of the floor thickness. In fact, the locations shown in Fig. 1 are from the flow noises and a mass of severe corrosion. The photo of the corrosion perforation is shown in Fig. 2. The photo indicates that the corrosion perforation was also caused by the lower surface corrosion of the tank bottom. It can be seen from Table 1 that the event rate in the whole group is 86.1, the event rate in the stronger zone is 105.4, and the event rate in the strongest zone is 202.8.

Three-dimensional locations of sources

Planar locations of sources

Fig. 1 Source locations of tank G503

Table 1 Analysis and result of storage tank G503 Positions/area m2 Whole floor/ 380.1 Positioning group/50.4 Stronger zone/ 27.0 Strongest zone zone/3.9

∑ Events 4884

Event rate 12.8

4337

86.1

2846

105.4

795

202.8

Defect description A corrosion perforation about φ10 mm at (1 m, 0), severe corrosion in a larger area with the max. Depth of 6.5 mm (80%)

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Fig. 2 Photo of corrosion perforation (G503)

The high event rate is caused by flow noise combined with extensive corrosion. When leakage occurs, the event rate is very high, the area of the main sound source group is usually large (greater than 10 m2), and the radiate positioning in the planar locations is an important indication of leakage. According to the needs of engineering, as long as the corrosion perforation of tank bottom is highly suspected or determined, the corrosion assessment is of little significance, because immediate shutdown will be needed to repair the perforation; the better quantitative MFL test will be adopted. In fact, some leaks are not necessarily observed on the spot, because the leaking medium may flow directly into the ground. For smaller leaks, the leakage may not be found by a flow meter or a liquid level meter. The case shows that AE testing results of the tank bottom are correct and the source locations are also highly consistent with the actual defects.

3.2

Case 2: A 4000 m3 Tank with Local Corrosion (0602#)

Tank parameters: atmospheric temperature; atmospheric pressure; size, φ18000  16,000 mm; volume, 4000 m3; floor thickness, 10/8 mm; material, Q235; age, 10-year-old; operating medium, octanol. Test condition: PAC DiSP AE workstation, 12 integrated sensors (R3I, 30–60 kHz) were evenly distributed along the tank wall circumference 200 mm from the base plate; testing liquid level is 12 m. The tank had been monitored for 11.3 h. The acoustic source locations are shown in Figs. 3, 4, and 5. Two significant sources were founded. According to AE testing results, we think that some severe

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Planar locations of sources

Fig. 3 Source locations of tank 0602 (0–4.0 h)

Three-dimensional locations of sources

Planar locations of sources

Fig. 4 Source locations of tank 0602 (4–11.3 h)

damages exist in the tank bottom and recommended that the tank be comprehensively inspected in the near future. MFL test of the tank floor was carried out after a period of time; two excessive corrosion defects were found on the plate 2-2 and the plate 3-2, respectively; and they were corroded on the lower plate surface, as shown in Fig. 6. The layout of the tank bottom is shown in Fig. 7. The locations of the main sound sources (S1, S2) detected by AE are highly consistent with that of MFL, which indicates that AE technique has higher positioning accuracy. The maximum corrosion depth of the lake-like corrosion on plate 3-2 is 70% of the wall thickness by MFL testing. The maximum corrosion depth of the

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S1

S2

Three-dimensional locations of sources

Planar locations of sources

Fig. 5 Source locations of tank 0602 (0–11.3 h)

S1

S2

Plate 3-2

Plate 2-2

Fig. 6 MFL results of plate 2-2 and plate 3-2

pitting on plate 2-2 is 44% of the wall thickness by MFL testing. The corroded steel plate was cut from the plate 3-2 to verify the inspection result. The photograph of the cut plate (with maximum depth of 6 mm) is shown in Fig. 8. The results are shown in Table 2. As can be seen from Table 2, the area of two sources is very small (less than 1 m2), but the peak is high. The event rate of S1 in the first phase (0 to 4 h) is 51.9, which is higher than the event rate of 36.4 in the second stage (4–11.3 h), indicating that the timing of the test may have an impact on the inspection results. The event rate of S2 in three stages is about 18. S1 is from the lake-like corrosion (see Fig. 8), and the maximum measured corrosion depth is 6 mm. Based on the analysis of above two cases, we believe that testing data of the first stage should be used to analyze or evaluate the safety of tanks with a smaller diameter. An acoustic source with a small area (less than 1 m2) and steep peak is likely to be pitting or lake-like corrosion.

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A1 A13 A2 2

A1

1/3

1/2

1/5

1/4

1/6

1/7 1/8

1/1

A3

A11

2/1

2/2

3/1

3/2

4/2

4/3

A10

5/1

A4

4/1

5/2

6/2

6/1

A5

7/1

7/8 7/2

7/3

7/4

7/5

7/6

7/7

A9

A6 A8

A7

Fig. 7 The layout of the tank 0602 bottom

Lower surface

Fig. 8 Photograph of the cut plate 3-2

Upper surface

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Table 2 Analysis and results of storage tank 0602 Position/area Whole floor, 254.5 m2 Group S1, 0.52 m2

Group S2, 0.26 m2

Holding time 0–4 h 4.0–11.3 h 0–11.3 h 0–4 h 4.0–11.3 h 0–11.3 h 0–4 h 4.0–11.3 h 0–11.3 h

∑ Events 1180 1115 2295 108 138 246 18 35 53

Event rate 1.2 0.6 0.8 51.9 36.4 41.8 17.3 18.4 18.0

Defect description Local corrosion

Max. measured depth of 6.0 mm, 75% Lake: 150  70 mm Max. depth of 3.5 mm, 44% Pitting: MFL

The event rate of over 50 may correspond to a corrosion of 70% and above of the plate thickness, and the event rate of 20–50 may correspond to a corrosion of 40–70% of the plate thickness. The centralized location in a large area often represents severe corrosion in the whole zone, and leakage may occur when the distribution of planar location points is radiate.

4 Conclusions 1. The similar evaluation method of tank safety based on AE online detection technology and verified template cases is proposed in this paper. The main evaluation criteria of similar tanks are the event rate of the acoustic source and the distribution characteristics of the acoustic source locations. 2. The event rate and the location shape discrimination adopted in this paper can be used for the severity evaluation of the similar tank floor corrosion and the leakage judgment of the similar tank floor. Radiate positioning is an important indication of leakage. 3. This is a “shared” evaluation method. All successful AE testing cases provided by colleagues can be used as templates. With the constant enrichment of various tank template cases, the AE detection and evaluation of tanks will be simpler and faster, which will promote the rapid popularization and application of AE online detection technology for tanks.

References 1. Machinery Industry Standards of the People’s Republic of China, JB T 10764-2007 Nondestructive-acoustic emission testing and evaluation of atmospheric pressure metal storage tanks 2. X. Yanting, W. Xiaowei, Effectiveness of AE on-line testing results of atmospheric vertical storage tank floors PVP2012-78067, July 2012

Acoustic Emission Testing and Evaluation of Ethylene Horizontal Tank Yadong Wang, Zhi Xiang, Yanting Xu, Jiele Xu, and Zhongteng Lai

Abstract Acoustic emission (AE) technique was applied to detect four ethylene tanks in a chemical plant. Based on the testing data, the AE sources were located and synthetically graded. Then the conventional testing methods were used to reinspect the acoustic emission sources. The reinspected results indicate that two cracks near the saddle are unaccepted and the others are faultless. The AE test results have good consistency with the reinspection results. Key words Ethylene · Horizontal tank · Acoustic emission testing · Location · Penetrant testing

1 Introduction Pressure vessels are pressurized equipments which are widely used in industrial areas of petroleum, chemical, electric power, metallurgy, etc. Their storage mediums normally consist of high temperature, high pressure, poisonous, and harmful gas or liquid that easily cause major safety accident [1]. Therefore, day-to-day operation of pressure vessel should grimly operate on the basis of different technological conditions to prevent dangerous situation of instability and failure of equipment, serious property damage, and casualty accidents. At present, our country has formulated a series of relevant laws and regulations which require regular inspection on in-service

Y. Wang (*) · J. Xu Zhejiang Provincial Special Equipment Inspection and Research Institute, Hangzhou, China Z. Xiang · Y. Xu Zhejiang Provincial Special Equipment Inspection and Research Institute, Hangzhou, China Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province, Hangzhou, China Z. Lai Zhejiang Safety Special Equipment Inspecting and Testing Co., Ltd., Hangzhou, China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_27

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pressure vessels to detect safety concerns early and guarantee the safe operation for long-period operation pressure vessels. A chemical enterprise has been using four ethylene tanks from 2004. According to the standard, the tanks are in the second comprehensive periodic inspection state. However, due to the plant requirement, they can’t be opened for testing. On the other hand, tank dumping, cleaning, and replacement would waste time and energy. High testing costs would cause certain economic losses and the waste of material resources. Therefore, according to the existing standards, Jiangsu Special Equipment Safety Supervision Inspection Institute looks forward to acoustic emission detection on the four ethylene tanks. And according to the AE test results, comprehensive evaluation is hopeful to be achieved. After that, the conventional test method was used to detect the tank in order to improve the detection efficiency, save the maintenance cost, and prolong life cycle of the storage tank.

2 Acoustic Emission Testing 2.1

Object

The test objects were four ethylene storage tanks which were marked as No. 1101, 1102, 1103, and 1104, respectively, in a petrochemical plant. The main relevant parameters of the tanks are shown in Table 1.

2.2

Inspection Standard

NB/T 47013.9-2012 Nondestructive Testing of Pressure Equipment Acoustic Emission Testing.

Table 1 Main parameters of test objects Design pressure

Operating pressure

0.1/2.3 MPa Design temperature 104–55  C Medium Ethylene

0–1.8 MPa Operating temperature 30–55  C Dimensions Ø4650  80119.4  30.5/ 16.2 mm

Maximum operating pressure 2.07 MPa Maximum operating temperature 55  C Material 12Ni19

Manufacture time 08/2004 Operating time 12/2004 Volume 1333 m3

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● 7# ● 52# (54#)

● 10# (12#)

● 1# ● 4# (6#) 58# ●

● 59# ● 56# (57#) ● 53#

8# (9#) ● ● 11#

2# (3#) ● ● 5#

Fig. 1 Sketch map of sensor arrangement

2.3

Equipment and Parameters

The detection instrument used in this test is SAMOS-80 channel AE detector which is produced by the PAC of the United States. Sensor is DT15I whose resonant frequency is 150 kHz with 35 dB gain. The threshold was set as 40 dB.

2.4

Sensor Layout Scheme

According to the structural features of the ethylene storage tank, 59 sensors were set on each tank, as shown in Fig. 1. The cylinder collocated with 19 ring sensors, each ring was staggered as 60 , each ring was distributed with three sensors, and each side of the spherical head was arranged with one sensor.

2.5

Loading Process

The loading medium is ethylene; the pressure curve is shown as Fig. 2.

3 Analysis of Test Results For four ethylene tanks (No. 1101–1104), AE online testing was carried out for nearly a month. Test results indicated that there were active AE sources in the tank wall. According to the AE testing standards [2, 3], AE data should be acquired and analyzed for getting comprehensive rating of different AE sources. According to AE testing results, these location sources were retested on external surfaces of 1101,

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Pressure (MPa) 



10min

30min 



10min

10min 



10min

10min

Time

min

Fig. 2 Loading process

Table 2 Location source evaluation and reinspection of four tanks Tank no. 1104

Location source no. C

Level III

Activity level Strength

Intensity level Medium

1103

A, B, E D A

II I III

Medium Weak Strength

Medium Medium Medium

B

III

Strength

Medium

1102

A

III

Strength

Low

1101

C A

II IV

Medium Strength

Medium High

D

III

Strength

Low

B, C

II

Medium

Medium

Reinspection No abnormalities – – Crack length 50 mm Crack length 10 mm No abnormalities – No abnormalities No abnormalities –

Remarks Outside surface PT and UT

Open tank, PT Open tank, PT Outside surface PT and UT Outside surface PT and UT Outside surface PT and UT

1102, and 1104, and no actual defects were found. On account of abnormal sound in the tank 1103, it was necessary to carry on opening inspection of the tank. The rating and reinspection of AE testing are shown in Table 2. The location sources of the tank 1104 are shown in Fig. 3. On account of abnormal sound sources of the tank 1103 during the detection process, the cylindrical positioning method was used to locate these sound sources, and the location sources are shown in Fig. 4. According to the actual situation on site, two level III AE sources as A and B were located near the saddle support. The external surface retesting was difficult, so the tank was opened for retesting. The area

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Fig. 3 Location sources of the tank 1104

Fig. 4 Location sources of the tank 1103

near the source A and B was detected by penetrant testing, and two cracks were found. There is a crack of 50 mm in the source A, and the photo is shown as Fig. 5. There is a crack of 10 mm in the source B, and the photo is shown as Fig. 6.

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Fig. 5 PT results of location source A at saddle 2

4 Conclusion 1. Acoustic emission testing technology is useful on special equipment online detection. Also, the AE sources can be located and comprehensively evaluated according to the detection data. 2. On the basis of ensuring the safety of equipment, acoustic emission online test can shorten inspection time, prolong the interval of inspection, and reduce maintenance cost for enterprises. 3. The acoustic emission test results are reliable and accurate. Also they are consistent with the conventional nondestructive testing results in this case.

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Fig. 6 Sketch map of the PT results of location source A at saddle 3

References 1. S. Gongtian, L. Bangxian, D. Qingru, Metal pressure vessel AE monitoring and safety assessment. Boil Pres. Ves. Saf. Chin. 14(4), 37–39 (1998) 2. TSG7001-2013, Periodic Inspection Rules for Pressure Vessels 3. JB/T 47013.9-2012, Periodic Inspection Rules for Pressure Vessels

Application and Research of Acoustic Emission in the Fatigue Test of HoopWrapped Composite Cylinders Yaping Liu, Gongtian Shen, Yadi Yan, Yang Li, and Yong Zhang

Abstract Hoop-wrapped composite cylinders for automotive vehicles increase rapidly in recent years. Therefore, hoop-wrapped composite cylinders for automotive vehicles increased rapidly. At present, several major accidents of compressed natural gas cylinders for vehicles have happened in China, but the hoop-wrapped composite cylinders are the most serious. In addition, in the regular inspection of hoop-wrapped composite cylinders for automotive vehicles, the ratio of surface damage has been found; at the same time, the surface of white spot damage in cylinders is much higher than in the past few years. Hoop-wrapped composite cylinders will be wound caused by collision with the vehicle surface damage in use, which affected the cylinder’s safety. Therefore, it is urgent to find an on service nondestructive testing method for the hoop-wrapped composite cylinders. Consequently, we study the acoustic emission (AE) signals characteristics of the hoopwrapped composite cylinders during the fatigue process, and fault recognition method initially is proposed. These studies have important significance for realization of AE online detection and fault diagnosis of the hoop-wrapped composite cylinders. Keywords Hoop-wrapped composite cylinders · Fatigue · Acoustic emission

1 Introduction The hoop-wrapped composite cylinders have been widely used in aerospace, petrochemical, and automotive industry [1, 2]. However, in recent years, there have been several major vehicle compressed natural gas cylinder accidents, in which the hoopwrapped composite cylinders are the most serious. In periodical surveys, the certain Y. Liu (*) · Y. Yan · Y. Li · Y. Zhang Baoding Special Equipment Inspection and Research Institute, Baoding, China G. Shen China Special Equipment Inspection and Research Institute, Beijing, China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_28

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ratio of harmful surface damage has been also found in the hoop-wrapped composite cylinders. The life of hoop-wrapped composite cylinders is limited. Generally, metal liners may crack or even leak after 100–1000 cycles, and thick-walled liners may leak after 10,000–30,000 cycles. Meanwhile, there is currently no means to detect the hoop-wrapped composite cylinders. AE which have advantages of dynamic testing, testing without material restrictions and high sensitivity, can be monitored in real time with equipment in use. Experimental study on AE of composite pressure vessels at home and abroad is in progress [3]. It can be concluded that it is feasible to evaluate AE as a dynamic evaluation method for hoop-wrapped composite pressure vessels. In the evaluation, the damaged of the hoop-wrapped composite cylinders will release the transient elastic wave when it is loaded; meanwhile the sensors receive and analyze the parametric characteristics of the elastic wave to evaluate the activity of the defect. The time difference of the waveform received by the sensors can be used to locate the defect. Therefore, researches of the AE characteristics of the hoop-wrapped composite cylinders which are running are necessary to improve the safety of CNG vehicles.

2 Experiment Method The fatigue test of the hoop-wrapped composite cylinders was carried out in accordance with the two experimental standards: GB/T 9252-2001 “Method for cycling test of gas cylinders” and GB 24160-2009 “Hoop-wrapped Composite Cylinders with Steel Liner for the On-board Storage of Compressed Natural Gas as a Fuel for Automotive Vehicles.” In the fatigue experiment, the sensors receive changes in the signal of the hoop-wrapped composite cylinders and then analyze the signals. Through the AE test of fatigue experiment, it is possible to detect the germination micro-cracks in the inner liner or wounded layer [4, 5]. With the increase of fatigue cycles, it is aware of the characteristics of AE signal from the generation and extension of crack and the location distribution in the hoop-wrapped composite cylinders [6, 7]. In the fatigue experiment, the AE system monitored the cracks in the inner liner or the composite layer. The position distribution features of crack generation and extended of AE signal can be found of the hoop-wrapped composite cylinders during the increasing number of the fatigue. In this process: 1. Observe the fatigue phenomenon and the AE signal characteristics of the hoopwrapped composite cylinders before 15,000 times of the fatigue process. 2. Understand the limit of the hoop-wrapped composite cylinders in the fatigue experiment. 3. Realize the fatigue phenomenon and the AE signal characteristics of the hoopwrapped composite cylinders which are leaked after 15,000 times of the fatigue process.

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Fig. 1 The three sensors were arranged in the hoop-wrapped composite cylinders

In the fatigue experiment, the AE measurements were realized with the AE system AMSY-6 including the broadband AE sensor VS900 and AE resonant sensor VS150. As shown in Fig. 1, the two sensors VS150 were arranged in the head and tail of the hoop-wrapped composite cylinders, and sensor VS900 was arranged at the tail. The distance between the two VS150 sensors is 1600 mm, which is arranged in a straight line. In the fatigue experiment, the pressure cycle is 7.89 times/min, and the AE signal is continuously collected until the hoop-wrapped composite cylinders are leaked (Fig. 1).

3 Experiment Results and Analysis For each fatigue cycle signals, the sum of hits and the mean value of other AE parameters are calculated by the statistical analytical method. During the fatigue experiment of the hoop-wrapped composite cylinders, the acoustic emission signals are also changed greatly with the increase of the fatigue cycle. By observing the statistical results of AE parameters, the energy, amplitude, RMS, and others gradually increase as the fatigue cycles increase. Figure 2 shows sensor VS900 and sensor VS150-RIC signal acquisition results. It shows that the parameters of the two sensors vary widely in statistics, but the data at the time of leak are consistent. It also shows that the acquisition capability of broadband AE sensor VS900 is better for the acoustic emission signal of hoopwrapped composite cylinders and is suitable for the application in the inspection during using.

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Fatigue Initial Stage

Figure 3 is the characteristics of the collected signals in the fatigue cycle of 30,100–30,600 times. During the 30,100–30,600 cycles of the fatigue cycle of the hoop-wrapped composite cylinders, firstly, the AE signals collected by the broadband sensor VS900 were analyzed with the fatigue cycle of 30,100–30,600 times.

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fatigue cycle 70100~70600

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Fig. 3 The characteristics of the collected signals in the fatigue cycle of 30,100–30,600 times

During this process, the number of AE signals was little, and there were no significant changes in the statistical parameters. By observing the waveform and frequency spectrum in Fig. 3b, it can be found that the typical AE signal covers a wide frequency range of 100–200 kHz. From the location in Fig. 3c, it can be found that the location signal has been more, and there are two more location signals. One is in the range of 200–300 mm, where the head of the hoop-wrapped composite cylinders is, and the other is mainly concentrated in the 800 mm, where the middle of the hoop-wrapped composite cylinders is.

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Fig. 4 The characteristics of the collected signals in the fatigue cycle of 40,100–40,600 times

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Fatigue Leak Stage

Figure 4 shows the characteristics of the collected signals in the fatigue cycle of 40,100–40,600 times. From the RMS, it can be found that the characteristic of peak in RMS is obvious, and the largest RMS signal is generated. In the waveform and frequency spectrum in Fig. 4b, we can see that the typical AE signal covers a wide frequency range of 100–200 kHz, which is mostly concentrated at 150 kHz. Through observation of the location in Fig. 4c, it can be found that the obvious crack signal appeared in hoop-wrapped composite cylinders after the fatigue cycle 18,000 times and the position is concentrated in the middle area of 1100–1250 mm.

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Cracking and Leaking Stage

Figure 5 is the characteristics of the collected signals in the fatigue cycle of 70,100–70,600 times. Two signals are obviously selected in the figures of energy duration and energy amplitude in Fig. 5a, b, which are named A and B. From the parameter diagram, it can be found that AE signal is more and all parameters are presented in the maximum state. During the fatigue cycle of 70,000 times of the hoop-wrapped composite cylinders, the crack signals of the hoop-wrapped composite cylinders are strong. As shown in the Fig. 5d, it can be found that with the increase of fatigue cycle, the location signal has been increasing, which is mainly concentrated in the middle area of 800–900 mm. We can obtain A and B signal information by filtering method shown in Fig. 6. By analyzing the A signal, it can be aware of that A signal is the main signal, which always exists in the fatigue process of the flawless hoop-wrapped composite cylinders. From the B signal, we can see that B signal is the peak signal, but the number is small. At the same time, it can be found that the B signal has the peak characteristic for all parameters with low amplitude, high energy, long duration, and rise time and high hits. Both A and B are low frequency signals which are at the range of 100–200 kHz and mostly concentrated at 150 kHz. Through Figure B signal (a) and (b), it can be analyzed that B signal shows a peak about every 50 μs.

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Fig. 5 The characteristics of the collected signals in the fatigue cycle of 70,100–70,600 times

Compared to the location of the signal of Fig. 7 and the actual leak location of the hoop-wrapped composite cylinders of Fig. 8, it can be discovered that they are basically the same location. By observing leakage signal, there may be multiple cracks, but it is most likely to happen to leak in the middle of the hoop-wrapped composite cylinders.

4 Conclusion During the fatigue experiment of the hoop-wrapped composite cylinders, the following conclusions can be concluded from the recent work about AE signals: 1. By observing the AE signals collected by the two sensors, it can be shown that with increase of the fatigue cycle, the AE signal changes, showing a trend of “rising slowly-rising faster-sharply rising.” 2. AE signals cover the frequency range of 100–200 kHz and are mainly concentrated at 150 kHz.

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E  D %signal Fig. 6 Signal A and B obtained information by filtering method

Fig. 7 The location of the signal

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Fig. 8 The actual leak location of the hoop-wrapped composite cylinders

3. Through analyzing the AE signal, it can be found that the AE technique could be used as an effective means for the safety evaluation of the hoop-wrapped composite cylinders. 4. The data of this experiment can provide the experimental data for the online testing of the hoop-wrapped composite cylinders by AE. 5. Observing the trend of statistical parameters of the AE signal of the fatigue of the hoop-wrapped composite cylinders, it can be analyzed the characteristic changes of defects or without defects. The method is preliminarily proposed to detect of the hoop-wrapped composite cylinders by the AE.

References 1. M. Toughiry, Examination of the Nondestructive Evaluation of Composite Gas Cylinders (The Nondestructive Testing Information Analysis Center, Washington, DC, 2002), p. 9 2. B. Kalaycioglu, M.H. Dirikolu, Investigation of the design of a metal -lined fully wrapped composite vessel underhigh internal pressure. High Pressure Res. 30(3), 428–437 (2010) 3. Z. Liu, L. Ge, et al., Acoustic emission testing of composite cylinders. Aerosp. Mater. Technol. (2011) 4. S. Erkal, O. Sayman, B. Semih, D. Tolga, Y. Emine Cinar, Fatigue damage in composite cylinders. Polym. Composites 31(4), 707–713 (2010) 5. S. Erkal, O. Sayman, S. Benli, et al., Fatigue damage in composite cylinders. Polym. Compos. 31 (4), 707–713 (2010) 6. T.M. Roberts, M. Talebzadeh, Acoustic emission monitoring of fatigue crack propagation. J Constr Steel Res 59(6), 695–712 (2003) 7. P. Veys et al., Fatigue analysis techniques for composite tank with plastically operating aluminum liners, in AIAA, SAE, ASME, and, ASEE, Joint Propulsion Conference, 17th Sacarment (1991)

A Method for Small Leak Precise Location in Pressure Piping by Acoustic Emission Ni Qin, Yongmei Hao, Xinming Yan, and Yunfei Yue

Abstract Due to leak location of acoustic emission (AE) signal affected by the surrounding noise and the signal with the dispersion and multimode characteristics in communication channels in pipeline, it is not accurate to use traditional AE technique for leak location. In this paper, scale analysis of AE signal de-noising by wavelet and the wavelet scales for selected db1, the horizontal level, is six layers. The continuous wavelet transform is used to analyze AE signal at time frequency and combined with modal acoustic emission (MAE) technique to reduce the impact of dispersion characteristics in signals. Finally, the exact position of the leak source is calculated by using the one-dimensional linear located formula. The experiment proved effectiveness of the method, making the positioning error of about 4%. Key words Pressure pipeline small leak · Location · Wavelet de-noising · Time-frequency analysis · MAE

1 Introduction The pipeline leak can occur due to long-term use of wear and tear, natural aging, man-made damage, and other factors. It is important to detect small leaks and the source of the leak’s location timely, and reduce the waste of resources and ensure the normal operation of the pipeline. Pipeline small leak can be considered that leak of less than 1.2% in the total pipeline flow or leak hole diameter is 1–5 mm [1]. The current typical locating pipeline leak detection methods are negative pressure wave method, pressure balance method, and so on. But there are some problems that have not been resolved, for example, based parameters by pressure and leak rate, the N. Qin · Y. Hao (*) School of Environmental and Safety Engineering, Changzhou University, Changzhou, China e-mail: [email protected] X. Yan · Y. Yue Changzhou Branch of Jiangsu Institute of Special Equipment Safety Supervision and Inspection, Changzhou, Jiangsu, China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_29

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sensitivity of small leak detection isn’t high and even can’t be detected, and detection to inside pipeline is vulnerable to the size of the pipeline and environment and other factors and is a high cost, based on magnetic flux leakage, ultrasonic, and other technique [2]. As a reliable and efficient leak location method, AE technique has a great advantage in dynamic detection and large area detection [3]. AE technique is applied in detecting and locating leak on pipeline. It cannot only detect under working condition but also locate the pipeline continuously leaking and tiny leak. However, there are certain problems. For example, the sensitivity of microleakage detection based on pipeline delivery pressure and leakage is not high or even detectable, and the in-tube inspection based on magnetic leakage, ultrasonic and other technologies is susceptible to pipeline size, surrounding environment, etc. factor influence and high detection cost. This paper proposed a method to deal with the AE leak signal based on the cross-correlation localization and one-dimensional linear time difference algorithm. The wavelet de-noising technique is used to reduce the influence of noise. The time difference of the leak signal propagation to the upstream and downstream sensors is determined, and the leak is obtained by time-frequency analysis. The frequency of the signal, combined with the modal acoustic emission technique to determine the mode of the leak signal, determines the propagation velocity of the AE signal in the pipeline, in order to calculate the location of the pipeline leak source.

2 Basic Theory 2.1

Pipeline Leak Location Theory

When pipe is leaking, due to internal and external pressure in the pipe, making the fluid in the pipe forms a jet at the leak hole, resulting in the normal flow of fluid disorders, and radiates energy produced by interaction of pipes and their media. The high-frequency sound waves are generated on the wall of the pipe, and the sound waves are propagated up and down along the official road. The leak can be located by analyzing two AE sensors arranged on the upstream and downstream of the pipeline, as showed in Fig. 1. The locating formula is: x¼

L  vð t 2  t 1 Þ 2

ð1Þ

where x is the distance between the leaking point and the upstream, L is the distance between the two pressure sensors, v is the propagation speed of an AE signal in the pipeline, and t1 and t2 are the points of time which the AE signal arrives at the upper and lower sensors from the leak. The time difference is Δt.

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Fig. 1 Design of pipeline leak detection and location system

2.2

Wavelet De-noising

Wavelet de-noising is to decompose the signal into high-frequency part and low-frequency part through the wavelet transform of signal at different scales. The energy of the original signal carrying information in frequency domain or wavelet domain is concentrated, as energy intensive regional signal decomposition coefficient absolute value is relatively large, and the noise signal of the energy spectrum is dispersed, and the absolute values of the coefficients is small. Thus, the wavelet coefficients with absolute value smaller than a certain threshold can be filtered by the action threshold method, so as to achieve the effect of noise reduction [4]. The process of wavelet de-noising can be divided into the following parts [5]: 1. Decomposition process—select a certain wavelet, the signal N-layer wavelet decomposition: A0 d ¼ A0 f þ ε  A0 z

ð2Þ

where A0d is the wavelet coefficient; d is the observed data vector; f is the true signal vector; z is the Gaussian random vector; and ε is the noise level. In this process, the linear transformation property of wavelet decomposition is used in the process. 2. Threshold value process—the decomposition of the various layers of the coefficient to choose a threshold, and the role of soft factor threshold processing; threshold processing of wavelet coefficients is s0d, such as the selected threshold form: TN ¼ ε

pffiffiffiffiffiffiffiffiffiffiffiffi 2logN

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The threshold processing can be expressed as ηNA0d, which proves that the soft-threshold processing of the wavelet coefficients using the threshold form when N tends to infinity can almost completely remove the noise signal in the observed signal. 3. Reconstruction process—the decomposition of the various layers of the coefficients obtained by wavelet reconstruction to restore the original signal. The inverse transformation of the processed wavelet coefficients A1 0 to reconstruct signal:

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The inverse wavelet transform is used to reconstruct the reconstructed signal: f  ¼ A1 0 ηN A0 d

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Time-Frequency Analysis

The continuous wavelet transform has a good time domain and frequency domain resolution. Therefore, continuous wavelet transform can be used to analyze the timefrequency of non-stationary random signals leaking from pipe. It can keep the time -frequency information of the signal and better understand the information of signal transmission. Assume that the AE signal of leak is the s(t) 2 L2(R) and ψ(t) is the wavelet basis function. The continuous wavelet transform is defined as:   C CWT ða; bÞ ¼ sðt Þ; ψ a, b ðt Þ ¼

  1 tb sðt Þpffiffiffi ψ dt a a 1

ð þ1

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where a 6¼ 0 is the scale parameter; b is the translation parameter; and ψ is the conjugate of Ψ. In the wavelet transform, the size of the frequency domain window changes with the scale change at any time t. When the scale parameter a becomes larger and the window width becomes wider, it is helpful to detect the low-frequency signal. When the scale parameter a is small, otherwise [6].

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Modal Acoustic Emission

Modal acoustic emission (MAE) theory states that the AE signal is composed of multimode wave and each mode of the wave is composed of a group of wide frequency waveforms, different frequencies of the waves spread at different speeds. By analyzing the ultrasonic model generated by the source, the inherent characteristics of the corresponding mode wave can be found, and the defect recognition and the estimation of the AE source can be carried out. According to the theory of guided wave propagation in the pipe, the leakage signal will propagate along the pipe wall, and the AE signal u(x, t) received at the distance from the leakage position x is [6]:

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 X   X  x x uðx; t Þ ¼ sn t  sn t þ ¼ cpn cpn n n

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where sn is the nth mode guided wave spectrum of the leakage excitation signal and cpn is the phase velocity of the nth mode guided wave propagation. The spectrum of signal u(x, t) with respect to time can be expressed as [7]: uðx; wÞ ¼

X

An ðwÞexp½iðxk n ðwÞ þ φn Þ

ð7Þ

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where An(w) and φn are the amplitude and phase of the nth mode guided wave in the leakage excitation signal, respectively; kn(w) is the wave number of the modal guided wave in the nth kn(w) ¼ w/cpn.

3 Pipe Leak Test In the simulation experiment, a kind of steel pipe is used to carry on the experimental research. This experiment used PAC-6006 AE instrument produced by PAC company, and the type of sensor is SR40M which is a low-frequency sensor. The pipe diameter is 165 mm, the length is 45 m, leak holes’ diameter is 1 mm, and the medium for the air and the tube pressure is 0.2 MPa. Sensor No. 1 and sensor No. 2 are placed at 2 and 42 m at 0 from the pipeline, and the test pipe leak is set at 18 m from 0. In the process of pipe leak experiment, the AE instrument in the process of data collection, as shown in Fig. 2, through the initial collection and sorting of the signal, can get the general positioning results, as shown in Fig. 3. From the above sound emission detection of the initial positioning map, it can be clearly seen that its position is in the 18 m, so there is a very significant AE leak signal. The RMS, ASL, and energy parameters detected by the AE instrument were filtered. Thereby locating the determinant positioning data with better positioning as shown in Table 1.

Fig. 2 The scene by AE signal collecting

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Fig. 3 Positioning by AE instrument Table 1 Pipeline leak experiment locate data sheet Serial number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Distance between sensor (m) 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40

Actual leak location (m) 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16

Measured location of the leak (m) 14.88 18.47 13.57 17.84 17.57 13.75 15.51 15.25 19.63 15.28 13.42 15.23 15.21 13.16 13.76 17.96 16.7 15.92 18.92 16.26 19.16 19.85 16.27 16.99 15.2 17.69 16.43 18.12

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4 Experimental Data Analysis and Processing The leakage signal was wavelet-degraded by Matlab software to reduce the impact of the noise signal. In this paper, we use dbN wavelet to analyze and deal with the acoustic emission leakage signal. At the same time, according to the actual effect of the leakage signal processing. It found that db1 wavelet is selected, and the number of decomposition layers is selected to be 6 layers. So this method is used to deal with the signal and take a group of upstream leak AE positioning signal, for example, analysis. The original signal is shown in Fig. 4 and after the noise reduction signal shown in Fig. 5. After the noise signal is processed, the singularity analysis of the processed signal is carried out. Firstly, the signal is decomposed by db1 wavelet, and the leakage signal is decomposed into six layers, and the singularity analysis is carried out. The upstream and downstream sensor positioning signals are analyzed after noise reduction, as shown in Fig. 6.

Fig. 4 Upstream 1 sensor original positioning signal

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Fig. 6 Sensor positioning signal results analysis. (a) Upstream 1 sensor signal results analysis. (b) Downstream 2 sensor signal analysis results Fig. 7 Group speed with frequency change

It can be obtained from Fig. 6 that the signal analysis sudden change point of the signal sensor No. 1 is about the 258th sampling point, and the signal analysis mutation point of the downstream sensor No. 2 is about the 267th sampling point. By analyzing the signal after noise reduction of the upstream and downstream sensors, the time difference is 0.009 s. Then the time-frequency analysis of the leak positioning signal after noise reduction is shown in Fig. 5. As can be seen from Fig. 5, the frequency is mainly about 10 kHz, and the group velocity is about 1 km/s in combination with Fig. 7. Then, the obtained group velocity and time difference are substituted into the leakage location formula (1), and the leak locating point is 15.5 m. The same results are analyzed for the remaining leaks of the pipeline leak test using the above method, as shown in Table 2 and Fig. 8. From Table 2 and Fig. 9, it can be seen that when the pipe leak hole is 16 m away from first sensor, the AE instrument detection is about 16.3 m and the error is 10.2%. The following results can be obtained by processing the acoustic emission detection

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Table 2 Experimental results of pipeline leak location signal analysis Serial number 1 2 3 4 5 6 7 8 9 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Average

Measure the location of leak (m) 14.88 18.47 13.57 17.84 17.57 13.75 15.51 15.25 19.63 13.42 15.23 15.21 13.16 13.76 17.96 16.7 15.92 18.92 16.26 19.16 19.85 16.27 16.99 15.2 17.69 16.43 18.12 16.3

Measure the relative error % 7.0 15.43 15.18 11.5 9.81 14.06 3.06 4.68 22.68 16.1 4.81 4.93 17.75 14.0 12.25 4.375 0.5 18.25 1.62 19.75 24.06 1.68 6.18 5.0 10.56 2.68 13.25 10.2

Analyzed the location of the leak after processing (m) 15.5 17.2 14.7 16.5 16.4 14.8 15.57 15.5 17.5 14.6 15.6 15.5 14.9 15 16.7 16.1 15.9 17.4 16.2 17.5 17.2 16.2 16.5 15.7 16.9 16.3 17.2 16.1

Post-processing relative error % 3.13 7.50 8.13 3.13 2.50 7.50 2.69 3.13 9.38 8.75 2.50 3.13 6.88 6.25 4.38 0.63 0.62 8.75 1.25 9.38 7.50 1.25 3.13 1.88 5.62 1.88 7.50 4.62

signal by the method in paper. The leakage is located at 16.1 m, and the error is 4.62%. Compared with the locating result obtained by traditional AE detection, this method reduces the leakage error of the pipeline by 5.58%. It can be seen that the method can effectively reduce noise and other factors on the impact of leakage positioning to achieve the precise location of the pipe leak location.

5 Conclusion By using the wavelet de-noising technique to reduce the noise of pipeline AE signal, and to reduce the dispersion of the AE signal of the pipeline and improve the resolution of the signal, the AE signal of the pipeline has better characteristics,

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which is helpful to improve the analysis result. The wavelet time-frequency analysis is combined with the MAE to accurately locate the leak location of the pipeline, and it is proved by experiments that the average error of pipeline leakage is 4.62%. The method can detect the leakage of the pipeline and locate it, which is of great significance to the practical application of pipe leak. Acknowledgment This paper was funded by the Project funded by Jiangsu Province Science and Technology Project (BE2014625), State Administration of Work Safety Project (jiangsu-00032016AQ), and Changzhou Municipal Science and Technology Project (CE20160024) (CM20179060). Thanks for the permission to publish this paper.

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References 1. Y. Xinming, H. Yongmei, W. Bao, et al., Features of small leakage occurred in urban transformation pipeline and analysis for its regularity. Proc. Equip. Pip. 53(295), 76–80 (2016) 2. C. Yuan, X.-F. Pang, Y. Liu, Status quo and prospect of pipeline leakage detection and location. J. Daqing Petrol Inst. 30(2), 76–79 (2006) 3. R. Long, K. Vine, M.J.S. Lowe, et al., Monitoring acoustic wave propagation in buried cast iron water pipes. Am. Instit. Phys. 557, 1202–1209 (2001) 4. C. Dong, Principle and Application of Matlab Wavelet Analysis Toolbox (National Defense Industry Press, Beijing, 2004) 5. S. Papadimitriou, A. Bezerianos, Multiresolution analysis and denoising of computer performance evaluation data with the wavelet transform. J. Syst. Archit. 42(1), 55–65 (1996) 6. S. Mallat, W.L. Hwang, W.L. Hwang, Singularity detection and processing with wavelets. IEEE Trans. Inf. Theory 38(2), 617–643 (1992) 7. X. Huiping, Wavelet and Spherical Wavelet Theories and Their Applications in Potential Field (Science Press, Henderson, NV, 2004)

Study of Acoustic Emission Attenuation Characteristics of the Steel Bifurcated Pipe in Hydropower Station Weiping Wu, Shengjin Cheng, Dongfeng Li, Shulin Cao, and Bo Lü

Abstract In order to study the attenuation characteristic of acoustic emission of the steel bifurcated pipe in hydropower station, the changing rules of acoustic emission signal waveforms with the increasing of propagation distance as well as the changes of the work surface are analyzed; considering the three main attenuation factors of the acoustic emission signal, a general expression of acoustic pressure in function of the propagation distance is proposed, which can be applied for different wave types of acoustic emission signals. Several fitting models of attenuation testing data are built, and a statistical parameter named adjusted coefficient of determination (adjusted COD or adjusted R-square) is used to evaluate the goodness of fit, and then the corresponding amplitude modification curves of the acoustic emission localization source are given. The user-defined nonlinear fitting model which is a combination of linear equation and logarithmic equation of amplitude-distance attenuation has the best fitting result. The results of the study can provide reference and guidance for similar projects. Keywords Acoustic Emission (AE) · Steel Bifurcated Pipe · Attenuation Characteristics · Curve Fitting · Goodness of Fit · Amplitude Modification

W. Wu (*) · D. Li · S. Cao National Center of Quality Inspection and Testing for Hydraulic Metal Structure Ministry of Water Resources, Zhengzhou, China S. Cheng North China University of Water Resources and Electric Power, Zhengzhou, China B. Lü Eddyfi China Co. Ltd., Chengdu, China e-mail: Blu@eddyfi.com © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_30

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1 Introduction Acoustic emission (AE) technology has been widely studied and applied in many industrial fields for nondestructive testing; however, the research and application in hydraulic steel structures are less developed. Acoustic emission technique can be used to dynamically monitor the hydraulic structures in real-time online [1, 2], the number of AE sensors required for AE testing depends on the size of the component and the spacing of the sensor array, and then, the sensor spacing is relied on the propagation attenuation characteristics of AE signals. Therefore, AE attenuation characteristics of the component are very important; meanwhile, the attenuation test result is pivotal in the amplitude modification of AE source. Domestic and overseas scholars have carried out a lot of researches on the attenuation characteristics of AE signal. Guan [3] studied the amplitude-distance attenuation law of AE signal in thin-wall plate and thick steel plate and obtained the attenuation coefficient which was recommended in engineering monitoring, but only the cylindrical wave was discussed. Wang [4] carried out the amplitude-distance and energy-distance attenuation test of the stainless steel runner of the hydropower station; just a simple linear fitting model was used taking material absorption attenuation into consideration. Kanji Ono [5] studied the attenuation of Lamb waves in carbon fiber-reinforced composites (CFRC); the fitting of amplitudedistance data of A0 and S0 modes of Lamb wave was analyzed by building the exponential decay model, which just considering the material absorption attenuation. Kassahun Asamene [6] had taken material absorption attenuation and geometrical diffusion attenuation into account when studying the amplitude-distance attenuation of CFRC sheet, the exponential decay model was adopted, but as for the geometric diffusion attenuation, it was limited to the specific wave type, which the sound pressure was inversely proportional to the square root of the propagation distance. Among the research mentioned above, the main research object was the flat plate or simple structure relatively, and there was no comprehensive consideration of the attenuation caused by the absorption, scattering, and the geometrical diffusion of different wave types nor did it involve research on amplitude modification of AE localization source. The steel bifurcated pipe in hydropower station has the characteristics of irregular structure, large local curvature, and thick plate. In this paper, the AE monitoring of hydraulic test of 600 MPa grade high-strength steel bifurcated pipe, which is in Xinjiang Qirehataer hydropower station of China, is taken as an example. Considering all kinds of main AE attenuation factors, the AE attenuation characteristic test and the amplitude modification are studied.

2 Waveform Analysis An asymmetric Y type of rib reinforced steel bifurcated pipe of Qirehataer hydropower station in Kashi City, Xinjiang Autonomous Region, China. The diameters of the main pipe and the branch pipe measure 3200 mm and 2200 mm, respectively.

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(a) Waistline Region

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(b) Fig. 1 Hydraulic test site of a steel bifurcated pipe. (a) Steel bifurcated pipe. (b) The K-shaped groove angle of crescent rib

The thickness of the pipe varies from 42 to 50 mm and the thickness of the crescent ribs is 120 mm. The material of the bifurcated pipe is 07MnMoVR steel, which the tensile strength and the yield strength are greater than 620 MPa and 490 MPa, respectively. Figure 1 is the hydraulic test site of the steel bifurcated pipe. In the hydraulic test of steel bifurcated pipe, the 4# sensor is placed on one side of the left pipe plate, and the distance away from the center of the crescent ribs along the thickness direction is 260 mm. The test uses a pencil lead break procedure to generate repeatable AE signals, and the pencil lead break position is about 100 mm from the center of the 4# sensor; the 4# sensor can receive the signal without passing through the weld or the ribs; after passing through the crescent ribs and the welds, the signal can be received later by the 3# sensor, which is placed on the opposite side with the distance of 910 mm away from the position of the pencil lead break. Figures 2 and 3 show the relevant waveforms and time-frequency distribution by continuous wavelet transform (CWT) [7].

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Fig. 2 The signal of propagation path not through the weld, with propagation distance of 100 mm and amplitude of 87.8 dB. (a) Graph of waveform. (b) The time-frequency distribution by CWT

According to Figs. 2 and 3, they show that the acoustic attenuation increases along the propagation distance. The welds and obstacles (the crescent rib protrudes from the weld, and the K-shaped groove angle of ribbed changes gradually) result in the interface reflection, the acoustic transmission path is complex, and each reflection generates wave type transformation besides superposition. With the increasing of propagation distance, the sharp sound waves are broadening, frequency shift is distinct (peak frequency in Figs. 2 and 3 are 149 kHz and 137 kHz, respectively), the frequency components are more abundant, and the rise time and duration of the signal become longer as well.

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Fig. 3 The signal of propagation path crossing the weld, with propagation distance of 910 mm and amplitude of 74.2 dB. (a) Graph of waveform (b) The time-frequency distribution by CWT

3 Theoretical Analysis and Mathematical Model Building Attenuation is the decrease of the amplitude or energy of the signal following the increasing propagation distance. There are many factors affecting the attenuation of sound waves [8], such as geometric diffusion attenuation, material absorption attenuation, scattering and diffraction attenuation, obstacle, dispersion effect, etc. Amplitude is an important parameter to indicate the intensity of the signal. The method of data fitting is often used to reveal and describe the relationship between

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amplitude and propagation distance, especially the least squares fitting method. Wu [9] carried out curves and surfaces fitting to analyze the experimental data; Weisberg [10] focused on building linear regression model, assessing fit and reliability, and drawing conclusions. The adjusted coefficient of determination [11] (adjusted COD or adjusted Rsquare) is used to evaluate the goodness of fit which considers the regression fitting models with unequal number of independent variables. Amplitude is usually expressed in dBAE. The formula is: dBAE ¼ 20log ðV=V ref Þ

ð1Þ

where dBAE is the amplitude expressed in decibels; V is the voltage (μV) of the preamplifier input or the sensor output; and Vref is the reference voltage and is 1 μV. In this paper, considering the geometric diffusion attenuation, material absorption attenuation, and scattering attenuation which are the three main attenuation factors, a general expression is used to express the sound pressure of the signal with a propagation distance of x, Px ¼ P0 =f ðxÞ  eαx

ð2Þ

where P0 is the sound pressure of the AE source; f(x) is the function of geometric spreading attenuation which is relevant to the propagation distance x; and α indicates attenuation coefficient. The geometric diffusion attenuation is related to the acoustic wave type; for example, the sound pressure Px of the spherical wave is inversely proportional to the propagation distance x, that is, Px / 1/x; the sound pressure of the cylindrical wave Px is inversely pffiffiffi proportional to the square root of the propagation distance x, that is, Px / 1= x. In order to take account the geometric spreading attenuation characteristics of different types of waves, the paper uses the following general expression to express the function f(x) which is related to the geometric diffusion attenuation, f ðxÞ ¼ mxn ðm > 0; n > 0Þ

ð3Þ

where x is propagation distance and m and n are parameters controlling geometric diffusion speed. Amplitude of the AE source can be expressed as A0 ¼ 20 log (P0/ 1μv) ¼ 20 log (P0), and the amplitude of signal with the distance of x away from the AE source can be expressed as Ax ¼ 20 log (Px/1μv) ¼ 20 log (P0)  20 log [f (x)]  α  x. The Ax can be measured directly by the detection system, but A0 can’t be obtained directly. The attenuation due to geometric diffusion attenuation, absorption attenuation, and scattering attenuation is represented by ΔAx: Ax ¼ A0  ΔAx

ð4Þ

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When the geometric diffusion attenuation is not considered, f(x) is equal to 1. The relationship between Ax and propagation distance x is presented by the following linear fitting equation: y ¼ y0  k  x

ð5Þ

where k is constant parameter obtained by the fitting equation. When considering diffusion attenuation, f(x) is a variable associated with x. Ax and x don’t meet the linear distribution any longer but nonlinear distribution. The following self-defined nonlinear fitting model (a combination of logarithmic and linear equations) is proposed: y ¼ y0  t  ln ðxÞ  k  x

ð6Þ

where y0, t, and k are constants obtained by the fitting equation.

4 Experimental Study of Acoustic Emission Attenuation Characteristics of Steel Bifurcated Pipe 4.1

The Amplitude-Distance Attenuation Test

The AE testing system used in the hydraulic test is the 38-channel Vallen AMSY-6, the digital frequency of the system is 10 MHz, and the time resolution is 0.1 μs. The type of the sensors is VS150-RIC of which the center frequency is 150 kHz; bandpass filter is set to 25–850 kHz. The output signals are amplified 34 dB by the amplifiers. The sensor array is completely arranged after the steel bifurcated pipe is filled with water. Pencil lead break procedure (0.3 mm, 2H) is used as the simulation source. The average value of the five-response amplitude of pencil lead break at the same position of a certain distance away from the sensor is taken as the amplitude of the signal at the distance. The nine data groups of propagation distance-amplitude are shown in Table 1. According to the data in Table 1, the amplitude attenuation fitting curve can be obtained as shown in Fig. 4.

Table 1 Attenuation test data of the steel bifurcated pipe Propagation distance (mm) Amplitude (dB)

100

87.0

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79.1

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77.6

1000

74.1

1500

71.5

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y 85

y

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0.01257 x 85.4 Adj.R-Square=0.68939

0.00445 x 81.1 Adj.R-Square=0.87227

Original Data Linear Fitting of Near-field Region Linear Fitting of Far-field Region Linear Fitting ExpDec1 Fitting User Defined Fitting

75 70 65

y

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The Adj. R-Square of Linear Fitting, ExpDec1 Fitting, and User Defined Fitting models are 0.87227, 0.88996, and 0.95747, respectively. It can be found that the fitting result of the user-defined fitting model is the best. Linear fitting model shows that attenuation is uniform with the increase of the distance, and nonlinear fitting models (ExpDec1 fitting, user-defined fitting) show that decay rate is gradually reduced with the increase of the propagation distance.

4.2

Amplitude Modification Curve of Acoustic Emission Source

According to the fitting equation of the user-defined fitting model, it shows that y tends to infinity when x tends to zero, but the maximum response amplitude of the acoustic emission instrument is 100 dB; meanwhile, according to the Chinese code of GB/T 18182-2012 [12], in order to facilitate the calculation, the amplitude of the AE source is not corrected when the distance between the acoustic emission source and the sensor is not more than 100 mm, the amplitude Ax ¼ 100 at 100 mm is used as the A0 (amplitude of AE source), and the modification value at x is the difference between the amplitude at the position of 100 mm away and the fitting value y at the position of x. The formula (4) can be transformed into: ΔAx ¼ Ax¼100  y

ð7Þ

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Amplitude Modification Curve Amplitude Modification (dB)

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When a simple linear fitting model is used, the modification value at x can be expressed as: ΔAx ¼ k  x

ð8Þ

where k represents the absolute value of the slope of the linear fitting equation. Based on the formulas (7) and (8), we can get the amplitude modification curves as shown in Fig. 5 of the two fitting models mentioned above. The attenuation rate of the linear fitting model is 4.45 dB/m. As for the user-defined fitting model, the calculation of amplitude modification is relatively complex, but the modification value is more accurate; when the propagation distance is short, the modification value varies nonlinearly with the propagation distance; when the propagation distance is long, the modification value varies linearly with the propagation distance approximately.

5 Conclusions In this paper, the AE attenuation characteristics of 600 MPa grade high-strength steel bifurcated pipe in Qirehataer hydropower station are studied, and the following conclusions are obtained:

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1. The steel bifurcated pipe is irregular shaped, the curvature is large, and the thickness of the plate is large. With the increase of the acoustic propagation distance, the initial sharp acoustic signal is being wider due to the multiple reflections and superposition of the waves, and the peak frequency is slightly shifting to the low frequency; in addition, the frequency components are becoming more abundant, and the rise time and the duration of the signal are becoming longer. 2. Considering the three main attenuation factors of geometrical diffusion attenuation, material absorption attenuation, and scattering attenuation, a general expression of acoustic pressure of AE signal with different propagation distance is proposed. 3. In the analysis of amplitude attenuation characteristics, the three fitting models of linear, exponential decay, and user-defined fitting (a combination of logarithmic and linear equations) are used to evaluate the goodness of the fitting by the statistical parameter-adjusted coefficient of determination; it is found that the goodness of the proposed user-defined fitting model is the best. The amplitude of the AE source is improved and the amplitude modification curves are given.

References 1. S. Cao, W. Zhang, G. Du, Acoustic emission testing technique application on hydro and hydropower projects. Dam and Safety 1, 30–32 (2004) 2. X. Wang, Several Key Problem Researches on Monitoring Cracks of Hydraulic Turbine Blades Based on Acoustic Emission Technique (Shanghai Jiao Tong University, Shanghai, 2009), pp. 1–119 3. W. Guan, Y. Tao, Z. He, Sound wave propagation and attenuation of thick-walled and thinwalled structures. Pres. Ves. 8(1), 48–53 (1991) 4. X. Wang, H. Hu, Y. Shao, et al. The distance attenuation of acoustic emission signals in turbine runners. Appl. Mech. Mater. 103, 262–267 (2012) 5. K. Ono, A. Gallego, Attenuation of lamb waves in CFRP plates. Acous. Emiss. 30(2012), 109–123 (2012) 6. K. Asamene, L. Hudson, M. sundaresan, Influence of attenuation on acoustic emission signals in carbon fiber reinforced polymer panels. Ultrasonics 59(2015), 86–93 (2015) 7. J. Vallen, H. Vallen, Latest improvements on Freeware AGU-Vallen-Wavelet, in Proceedings of 31st Conference of EWGAE (EWGAE 2010) (2010), pp. 1–8 8. M. El-Shaib, R. Reuben, T. Lim, Predicting acoustic emission attenuation in small steel blocks using a ray tracing technique. Insight 54(12), 673–680 (2012) 9. W. Wu, Sub-pixel Displacement Algorithm Based on Image Correlation Method and Experiment Study (Huazhong University of Science & Technology, Wuhan, 2009), pp. 16–23 10. S. Weisberg, Applied Linear Regression, 4th edn. (John Wiley & Son, Inc, Hoboken, NJ, 2014) 11. H. Jin, S. Gao, Selection of best multiple linear regression model. Journal of Hebei University of Technology 31(5), 10–14 (2002) 12. Standardization Administration of the People’s Republic of China, GB/T 18182–2012, Acoustic Emission Examination and Evaluation of Metallic Pressure Vessels (Standards Press of China, Beijing, 2012)

Research on Pipeline Fault Diagnosis Technology Based on Automatic Encoder Xinying Wang, Xingshuai Song, Taiwang Yang, Huiran Zhang, and Haiqun Chen

Abstract The extraction method of acoustic emission characteristic parameters is the key technology in the process of pipeline fault diagnosis. However, with the development of artificial intelligence and large data, how to effectively extract and utilize the characteristics of pipeline fault is an important factor to ensure the correct rate of pipeline fault diagnosis. In this paper, a pipeline fault diagnosis method based on the sparse automatic encoder is proposed, and the pipeline fault classification model is established and tested with four kinds of data sets. Finally, the pipeline fault diagnosis test is carried out under the laboratory conditions, and the method is proved. Keywords Classification deep learning neural network · Sparse automatic encoder · Recognition accuracy

1 Introduction References [1, 2] show the acoustic emission signals generated by the oil and gas pipelines in different transport states are different; the detection of the pipeline by the acoustic emission signal becomes an effective method in the nondestructive monitoring. References [3–5] show artificial intelligence technology and compute have rapid development in recent years; experts at home and abroad based on the collected acoustic emission signal proposed many experimental schemes on fault of pipeline. For example, References [6–10] show backpropagation neural network (BPNN), DS fusion, support vector machine (SVM), and other methods of intelligent diagnosis of pipeline failure. However, the above method requires a high degree of integrity of the sample, so it is not possible to make full use of a large number of unlabeled data in the acoustic emission detection system.

X. Wang (*) · X. Song · T. Yang · H. Zhang · H. Chen School of Environment and Safety Engineering, Changzhou University, Changzhou, China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_31

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Reference [11] shows that deep machine learning was proposed by Professor Geoffrey Hinton in “Science” published in 2006; deep learning neural network (DLNN) is one of them, and strong automatic learning ability is its biggest characteristic, which is mainly reflected in the input data layer-by-layer characteristics of learning and can reconstruct new features, and artificial selection and extraction of features are avoided. Therefore, References [12–14] show DLNN has gradually become one of the focuses of research at home and abroad in recent years. In this paper, the automatic coder in the deep learning neural network is selected as the foundation model, the fault diagnosis model of the needle for the pipe is established, and the experiment is carried out under the laboratory conditions. The results are compared with the BPNN and SVM to verify the reliability of the method.

2 Automatic Encoder According to the course notes of Professor Andrew Ng of Stanford University in the United States [15–18], the automatic encoder is a neural network that outputs the signal instead of the input signal. In order to achieve the output signal and the input signal as much as possible, the automatic encoder must be extracted with the original data of the essential components of the characteristics of the input signal. The specific steps are as follows: firstly, the non-/semi-supervised way to learn the essence of unlabeled data; secondly, the output characteristics of the encoder are used to train the next layer and thus cycle layer-by-layer training; finally, carrying out supervised fine-tuning. For general purposes, the most basic AE is a three-layer neural network, as shown in Fig. 1, X is the input layer, h is the hidden layer, and Y is the output layer, and X and Y generally have the same nodes. The conversion process from X to h is generally referred to as encoding, and the conversion from h to y is called decoding, as shown in Fig. 2. Suppose f is the encoding function and g is the decoding function, then the expression of the two functions is as follows: h ¼ f ð x Þ ¼ S f ð W x þ pÞ
 y ¼ gðhÞ ¼ Sg W T þ q

ð1Þ ð2Þ

where Sf and Sg general take the Sigmoid function; Wx is the weight matrix between x and h; WT is the weight matrix between h and y; p is the offset vector for h; and q is the offset vector for y. In order to express the feature more accurately, the AE parameters W, p, and q are abbreviated as θ. It is assumed that the training set S ¼ {x1, x2, . . ., xn}, the pre-training for AE is practically trained by S. Therefore, it is common to define the target after training, that is, the output y should be approximated to the input x, and the reconstruction error function L(X, Y) can be used to represent the degree of approximation, where L(X, Y ) can be defined as:

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Lðx; yÞ ¼

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L(X, Y ) can be used to represent the degree of approximation, where L(X, Y ) can be defined as in Eq. (3). Equation (4) can be expressed as a loss function based on Eq. (3) on training sample set S, and the parameters of each layer AE are obtained by minimizing the processing of Eq. (4). 1 X J AE ðθÞ ¼ L½x; gðf ðxÞÞ ð4Þ N x2S where JAE(θ) is the loss function of θ, and N is the number of samples input for the training sample. However, for most practical applications, if only the loss function is only minimized, it is often possible to obtain a constant function. In order to avoid this situation, a variant of an automatic encoder—sparse encoder is used for processing.

3 Pipeline Fault Diagnosis Model Based on CDLNN 3.1

Depth Learning Neural Network Classification Model

In this paper, a deep learning neural network (CDLNN) model is established. It is composed of input layer, several SAE layers, classification layer, and output layer. Among them, several SAE layers are in the form of stack existing. The depth of learning neural network classification model framework is shown in Fig. 3. In order to solve the problem of multi-classification, we choose SOFTMAX as a classifier, and because the classifier can output the probability of classification results, and can be better combined with SAE, it is usually can get better classification effect. CDLNN to solve the problem of multi-classification training process is usually pre-training and fine-tuning of the two processes. The first step in pre-training is to select the unlabeled data or label data to construct the input samples and then use the BP algorithm to initialize a series of AE parameters. Fine-tuning is the use of tagged

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Table 1 CDLNN classification of four data sets Data set Adult Car Evaluation Iris Wine

Training set 200 800 600 600

Test set 100 200 100 150

Average classification accuracy rate/% 95.57 97.39 98.26 96.49

samples to tune the entire network parameters. The error of the input data sample is minimized, so that the recognition performance of CDLNN is optimized.

3.2

CDLNN Model Classification Performance Test

Using the established CDLNN model, Iris, Adult, Wine, and Car Evaluation were used to classify the four classical machine learning datasets. The initial learning rate was set to 0.1, and the network parameters were initialized to follow the random smaller values of the Gaussian distribution. The parameter update rate value is set to 0.01. Table 1 shows the classification of CDLNN for four different datasets. It can be seen from the column of average classification accuracy that CDLNN has achieved satisfactory results for the classification of each data set, which shows that the established CDLNN model can be used to solve the problem of multi-classification.

3.3

Select the Sample Data

In order to keep the sample set too skewed and to ensure that sufficient data samples are available, the acoustic emission data collected by the recent laboratory leak test is used as a pre-training sample because the data is not tagged. Laboratory gas pipeline leakage test, with broken lead to simulate the pipeline when the pipeline issued by the acoustic emission signal, with gauze friction to simulate the pipeline cracks occurred when the pipeline issued by the acoustic emission signal, opens the leakage valve to simulate the gas pipeline leakage state when the pipeline issued by the acoustic emission signal and collected under normal transport conditions issued by the pipeline acoustic emission signal. Since the fine adjustment requires a small number of labeled samples, the data obtained when the pipe failure is simulated is taken as the sample required for fine-tuning.

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Select the Feature Variable

According to the data collected by the acoustic emission detection system, the amplitude, absolute energy, ringing count, rise time, duration, average signal level, and effective voltage are selected according to the CDLNN model’s automatic learning, conversion characteristics, and classification ability. And the event counts the characteristic variables of the eight kinds of reaction pipeline operating states. In order to make the difference between the characteristic variables as narrow as possible, and in order to make the calculation more accurate, the various features are normalized by the formula (5). x  xmean xnew ¼ ð5Þ xstd where xnew is the normalized characteristic value; x is the original value of the characteristic parameter; xmean represents the mean value of all the parameter values in each sample set of x; and xstd is the standard deviation of the characteristic parameter of the sample set.

3.5

Pipeline Status Coding

With reference to the failure of the pipeline in the actual transport process, the fault diagnosis can be summarized into four categories; Table 2 shows the detailed fault type and coding.

3.6

CDLNN Model for Pipeline Fault Diagnosis

Figure 4 shows the structure of the pipeline fault diagnosis CDLNN model. The input model is the seven characteristic parameter values that have been standardized, and the output model is the probability size of the pipeline transport states. The corresponding state of the maximum probability is the result of the fault diagnosis. Pipeline fault diagnosis CDLNN model training process and CDLNN similar, mainly divided into pre-training and fine-tuning two processes. The seven characteristic parameters that have been standardized are input into the model, and a series Table 2 Pipeline running status encoding

Pipe running status Normal Pipeline leaks Pipe crack Pipe fracture

Coding (0,0,0,2) (0,0,2,0) (2,0,0,0) (0,2,0,0)

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Amplitude Absolute energy Normal Counts Rise time Duration

Leak

Crack

Average signal level Crackle RMS voltage

Fig. 4 Pipeline fault diagnosis model based on CDLNN

of AE parameters are initialized with a large number of training samples. The parameters are then fine-tuned to adjust the entire network with a small number of data samples with labels. Therefore, the fault diagnosis effect of the deep learning neural network is optimized, and finally the probability of each transport state of the pipeline is output, and the pipeline running condition corresponding to the maximum probability is the diagnosis result.

4 Implementation Steps of Pipeline Fault Diagnosis The CDLNN-based pipeline troubleshooting procedure is as follows: 1. Collecting pipeline fault data: on the measured pipeline to be broken lead, gauze friction, open the leakage valve and other incentives, and collect different fault states of the acoustic emission signal in the acoustic emission acquisition system. 2. Data preprocessing: standardized data for different types of fault data collected and classified as training samples and test samples. 3. Coding and CDLNN model: coding the different fault states for the pipeline and constructing the CDLNN model. 4. Initialize the network: the network structure and related parameters to initialize generally include SAE network layer, learning rate, eigenvector dimension, and the number of iterations.

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Fault raw data acquisition and preprocessing

diagnostic result

SOFTMAX

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Pipeline to be tested

Excitation signal

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Acoustic emission detection system

Sensor 1

Fig. 5 Flowchart of pipeline diagnosis based on CDLNN

5. SAE pre-training: the input of the pipeline fault sound emission signal training sample set layer-by-layer training stack from the encoder until the convergence criteria to reach the end of training. 6. Global fine-tuning: use the BP algorithm or CD algorithm to enter the stack selfencoder feature output and the corresponding category label to train the entire network. 7. Diagnostic performance test: the training of the network is saved and with the selected test samples for fault diagnosis test. 8. Output diagnostic results (Fig. 5).

5 Experiment and Result Analysis 5.1

Experimental Design

In order to verify the effectiveness of the method, two acoustic emission sensors were placed on both sides of the simulated pipeline fault point of the laboratory pipeline leakage acoustic emission detection system. The signals of the pipeline were normal, leaked, broken, and cracked. Figures 6 and 7 are laboratory piping leaks, acoustic emission detection systems, and acoustic emission sensors on both sides of the fault points. The laboratory pipeline leak detection system in Fig. 6 is composed of data acquisition and processing, pipeline storage and transportation units, and measuring instruments composed of three units, measuring instruments for the PCI-II acoustic emission card, S/N2462026504 amplifier, and R15 single-ended wideband acoustic emission sensor. Figure 7 shows two R15 single-ended wideband acoustic emission sensors, which are placed on both sides of the pipe failure point. The experimental data acquisition process is as follows: air compressor for the pipeline to provide air simulation gas pipeline, the pipeline under test lead, gauze friction, open the leak valve and other operations to simulate the pipeline fracture, cracks, leakage, and no operation of the pipeline to simulate the pipeline normal operation. The acoustic emission data are collected by two R15 single-ended wideband acoustic emission sensors in Fig. 7, and then the wavelet analysis method is used to extract the data of two sensor signals. The seven characteristic parameters,

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Fig. 6 Laboratory piping fault diagnosis simulation system

Fig. 7 Two acoustic emission sensors near the point of failure

such as amplitude, absolute energy, ringing count, rise time, duration, average signal level, and RMS voltage, are used as the raw data of the input model. Because the wavelet transform technology can better identify the acoustic emission signal and noise, this paper selects this technology to diagnose the fault of the gas pipeline. The diagnosis process mainly includes the steps of decomposing and denoising the signal and extracting the characteristic parameters. 1. Selecting the wavelet base to decompose the initial acoustic emission signal: mainly selecting a suitable wavelet base, determining the number of layers (N) to be decomposed, thereby decomposing the original signal. 2. Eliminate signal noise: use different denoising methods to process the highfrequency coefficients of the first to N layers of the signal (including threshold processing). 3. Reconstructing the acoustic emission signal: combining the low-frequency coefficient after decomposing the N layer and the N layer high-frequency coefficient after denoising, reconstructing the real acoustic emission signal.

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Fig. 8 CDLNN-based pipeline fault diagnosis based on AE when the number of layers is 0–10

In this experiment, 2500 groups of normal, faulty and approximate fault samples were selected as pre-training sets. A total of 600 sets of fault and normal status tagged data are used as the fine-tuning set and test set, and the ratio of the fine-tuning set and the test set is 2:1. The pipeline fault diagnosis was tested as follows using the established CDLNN model.

5.2

Experimental Results and Analysis

1. Determination of the number of SAE layers. In the actual situation, the number of layers of AE is not fixed, but is selected after continuous trial based on the currently solved problems, for the pipeline fault diagnosis problems in order to select 0–10% of the number of layers for diagnosis Test, the diagnostic effect shown in Fig. 8. It can be seen from the curve in Fig. 8 that when the number of AE layers reaches 4 layers, the average diagnostic accuracy rate of the pipeline failure is already very high, and as the number of AE layers is gradually increased, the average diagnostic accuracy rate is slowed down, and the training time of the CDLNN model also becomes longer, so in order to get the best diagnosis with the least training time, the number of layers of AE is set to 4 layers in the experiment. 2. CDLNN for different pre-training set of pipeline fault diagnosis. CDLNN for different pre-training set of pipeline fault diagnosis as shown in Table 3. As can be seen from Table 3, the average correct rate of CDLNN pipeline failures is continuously improved as the number of pre-training sets increases. When the pre-training set reaches 2500, the average correct rate of pipeline faults has reached over 90%, which indicates that CDLNN has better fault diagnosis performance and is suitable for diagnosing different pipeline faults. There are many methods used in pipeline fault diagnosis, and other commonly used methods are BPNN algorithm and SVM algorithm. The two methods are

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Table 3 Pipeline fault diagnosis of CDLNN in different pre-training sets Pre-training set 500 1000 2000 2500

Fine-tune set 600 600 600 600

Test set 300 300 300 300

Average correct rate/% 80.25 85.37 88.72 91.76

Table 4 Pipeline fault diagnosis based on SVM and BPNN in different training sets Algorithm BPNN

SVM

Training set 200 300 450 600 200 300 450 600

Test set 300 300 300 300 300 300 300 300

The average accuracy of the diagnosis/% 58.37 63.53 78.46 82.64 63.74 77.68 86.27 87.21

tested under the same experimental conditions and compared with the algorithm in this paper. The test results are shown in Table 4. The maximum training iteration number of epochs is 1500, the learning rate vIr is 0.01, the regularization coefficient C of SVM is 2048, the kernel function parameter gamma is 0.03, and the kernel function of the vector machine uses the RBF function. Tables 3 and 4 show comparing the results of pipeline fault diagnosis in, we can see that the average fault diagnosis rate of this method is the highest. SVM is better than that of traditional BPNN, but SVM generally applies to the two classification problem; as for the multiclass problem, there are some shortcomings in finding the optimal parameters, and it is necessary to rely on many experiments to get the ideal discriminant effect. The algorithm is better than SVM and avoids the artificial extraction of BPNN and SVM and selects the feature of the process, from the input data automatically layer by layer to learn the characteristics of the reconstruction of the characteristics of the failure to reproduce the characteristics of the fault signal and input to the multi-classification problem in the classifier SOFTMAX classification. Therefore, compared with BPNN, SVM has better diagnostic effect in pipeline diagnosis and can provide a more scientific reference for judging whether the pipeline is faulty or not. In addition, the author found that the average accuracy of CDLNN pipeline failure will increase with the increase of training set; when the degree reaches a certain degree, the accuracy rate will increase significantly slower and SVM and BPNN method at the time of diagnosis. When the training set reaches 600, the average accuracy of the failure is basically the same, which shows that the use of semi-supervised learning CDLNN than the use of supervised learning SVM and BPNN method has a stronger learning ability and scalability.

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6 Conclusion 1. The pipeline fault diagnosis method based on the automatic encoder solves the problem of the manual selection feature existing in the traditional pipeline fault diagnosis method. 2. Using CDLNN model to diagnose gas pipeline, the result shows that the accuracy rate is 91.76%, which greatly improves the accuracy of pipeline fault diagnosis. 3. Under the same experimental conditions, compared with SVM and BPNN methods, this method highlights the intelligence and stability of the method. Acknowledgment This study was supported by the key technology and technology project for the prevention and control of major accidents in the safety production of the State Administration of safety supervision ([2013]140, Jiangsu -19) and Changzhou science and technology support program (CZ20170017) and the key subject of education and teaching research (GJY2014003) of Changzhou University.

References 1. Y. Rui, Research on Feature Extraction and Recognition Method of Leakage Acoustic Emission Signal (Kunming University of Science and Technology, Kunming, 2016) 2. S. Li-Ying, L. Yi-Bo, Q. Zhi-Gang, et al., Research on acoustic emission pipeline leakage detection based on EMD signal analysis method. J Vibrat Shock 26(10), 161–163 (2007) 3. Y. Hong-Yu, Research on Pipeline Leakage Diagnosis Method Based on Wavelet Analysis and BP Neural Network (Northeastern University, Shenyang, 2009) 4. S. Xiao-Song, T. Xing-Qiang, Detection and location of pipeline leakage neural network based on wavelet decomposition. China Petrol Mach 34(8), 56–58 (2006) 5. L. Qun-Yang, X. Fu-Tang, G. Shunfeng, et al., Fault diagnosis of mechanical system based on BP neural network. Saf Environ Eng 22(6), 116–119 (2015) 6. W. Xin-Ying, J. Zhi-Wei, Y. Yong-Liang, et al., Research on multi - information fusion urban gas pipeline leakage diagnosis technology. China Saf Sci J 24(6), 165–171 (2014) 7. L. Ai, J. Wang, X. Wang, Multi-features fusion Diagnos is of tremor based on artificial neural network and D-S evidence theory. Signal Process 88, 2927–2934 (2008) 8. L. Zheng-Shan, B. Ao-Rui, W. Xiao-wan, Corrosion prediction of high sulfur oil - gas pipeline based on PCA – SVM. China Saf Sci J 26(2), 85–89 (2016) 9. Y. Qi, N. Rui-Qing, Z. Jin-Tong, et al., Research on landslide displacement prediction method based on empirical mode decomposition - support vector machine. Saf Environ Eng 24(1), 26–31 (2017) 10. I. Guyon, J. Weston, S. Barnhill, et al., Gene selection for cancer classification using support vector machines. Mach Learn 12(3), 222–225 (2006) 11. G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313(5786), 504–506 (2006) 12. Y. Bengio, Learning deep architectures for AI. Found Trends Mach Learn 2(1), 2–125 (2009) 13. G.E. Hinton, S. Osindero, Y.W. Teh, A fast learning algorithm for deep belief nets. Neural Comput 18(7), 1527–1554 (2006) 14. G.E. Hinton, A practical guide to training restricted Boltzmann machines. UMTL Tech Report 2010-003 (University of Toronto, Toronto, ON, 2010) 15. J. Baker, L. Deng, J. Glass, et al., Developments and directions in speech recognition and understanding, Part 1. IEEE Signal Process Mag 26(3), 76–80 (2009)

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16. D. Yu, L. Deng, Deep learning and its applications to signal and information processing. IEEE Signal Process Mag 28(1), 146–153 (2011) 17. Z. Kalal, K. Mikolajczyk, J. Matas, Face-TLD: tracking-learning-detection applied to faces. In: International Conference on Image Processing (ICIP 2010), Hong Kong, China (IEEE, Washington, DC, 2010), pp. 3789–3791 18. A. Ng, UFLDL Tutorial. http://openclassroom.stanford.edu/MainFolder/CoursePage.php? course¼DeepLearning

Acoustic Emission Testing of a Friction Stir Welding Aluminum Alloy Pressure Vessel Jun Jiang, Cheng Ye, Zhongzheng Zhang, and Yongliang Yu

Abstract To study the acoustic emission (AE) characteristics of an aluminum alloy pressure vessel manufactured by use of a friction stir welding (FSW) method, a tubular aluminum alloy pressure vessel containing an incomplete penetration defect was monitored by AE technology during hydraulic blasting testing. The characteristic parameter analysis method and fracture surface morphology observation were used to analyze the AE signal characteristics. The results show that the characteristic parameters of the AE amplitude, signal count, and energy jumped significantly when a large expansion of the defect occurred, and they stayed at a high level with the rise in pressure. AE technology can effectively monitor FSW aluminum alloy pressure vessel incomplete penetration defects and provide early warning. The results of this study are of great relevance to the detection of incomplete penetration defects in FSW aluminum alloy pressure vessels. Keywords Friction stir welding · Aluminum alloy · Acoustic emission · Pressure vessel

1 Introduction Friction stir welding (FSW) is one kind of new solid-phase connection technology used for application of low-melting alloy sheets. In comparison with the traditional melting welding method, it results in lower residual stress and deformation of the joint after welding, and it is difficult for pores and hot cracks to form in the joint [1]. In recent years, FSW technology has been used in the manufacture of aluminum alloy pressure vessels, especially for elongated tubular vessels, and can greatly improve the work efficiency. However, if there is improper control of the welding

J. Jiang (*) · C. Ye · Z. Zhang · Y. Yu Nanjing Boiler and Pressure Vessel Inspection and Research Institute, Nanjing, China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_32

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process, it is easy to produce some typical defects, such as incomplete filling, incomplete penetration, and root discontinuity. These defects have obvious close, subtle, complex characteristics, and it is not easy to achieve satisfactory results with the application of conventional x-ray and ultrasound nondestructive testing methods. In view of the successful application of acoustic emission (AE) technology in the field of pressure vessel inspection [2–13], AE detection technology has been introduced for use in FSW aluminum alloy pressure vessels, and the feasibility of using it to test the quality of an aluminum alloy pressure vessel is discussed here. In this study, one aluminum alloy pressure vessel with incomplete penetration was monitored by an AE method during water pressure testing and the characteristics of the AE signals were studied during expansion of the welding defect. The results of this research can provide technical references for AE technology for manufacturing quality testing and in-service inspection of aluminum alloy pressure vessels.

2 Test Process The test vessel is one new aluminum alloy tubular vessel. It includes one longitudinal weld on the cylinder and two circumferential welds on the ends of the cylinder, which connect to the two end flanges by means of FSW. The container is made of aluminum alloy 5083, the specifications are Ф 800 mm
17,000 mm
10 mm, and the design pressure is 0.6 MPa. Both ends of the container are sealed with carbon steel flat heads and flanges with bolts. A water inlet is provided at one end of the plate head to connect the pressure gauges and the hydraulic pressure test machine. The container is placed on a saddle-type support and is surrounded by a safety protection network. The AE equipment is an AMSY-5 model from the German Vallen Company. This system consists of 50 separate monitoring channels for gathering AE information. The SR150 resonant sensor is selected with a resonant frequency of 150 kHz and a bandwidth of 100–850 kHz. The time difference positioning method is used in the test. The sensor array is an isosceles triangle, where the circumferential spacing is 94 cm, the axial spacing is 150 cm, and the number of sensors is 39. The aluminum alloy container and sensor layout are shown in Fig. 1. The test threshold is set at 40 dB. Using one pressure cycle, loading the way for the ladder load, the loading process is as listed in Table 1. During the test, AE signals are observed. In the continuous loading process, when the pressure exceeds 0.6 MPa, an abnormal and intermittent metallic sound can be heard; when the pressure reaches 1.7 MPa, with container blasting failure, AE data collection is stopped.

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(b)

Fig. 1 (a) Test aluminum alloy pressure vessel. (b) Schematic diagram of acoustic emission sensor arrangement. X-Loc. location Table 1 Load versus time during the test Time (s) 0 60 120 180 240 300 360 420 480 540 600 660 720

Pressure (MPa) 0 0.05 0.09 0.13 0.18 0.23 0.28 0.33 0.39 0.44 0.50 0.54 0.60

Time (s) 780 840 900 960 1020 1080 1140 1200 1260 1320 1380 1440 1500

Pressure (MPa) 0.60 0.60 0.61 0.65 0.71 0.76 0.81 0.86 0.91 0.96 1.00 1.05 1.1

Time (s) 1560 1620 1680 1740 1800 1860 1920 1980 2040 2100 2160 2220 2280

Pressure (MPa) 1.14 1.19 1.23 1.27 1.31 1.4 1.45 1.49 1.54 1.58 1.63 1.67 1.70

3 Test Results and Discussion 3.1

Characteristic Parameter Distribution and Analysis

The AE characteristic parameter analysis method is one of the main methods used to characterize and evaluate the results of AE testing on pressure vessels. Common feature parameters include the amplitude, rise time, duration, energy, and signal count. The AE signals analyzed in this study were produced by the representative S28 sensor, which was close to the maximum burst in this experiment. The main characteristic parameters are shown in Fig. 2. Amplitude is a very important characteristic parameter in AE technology. Because it is directly related to the mechanism of the AE source, it is often used to identify the AE source and to evaluate the results of AE detection. Figure 2 and Table 1 show that at 0–495 s, with a corresponding pressure of 0–0.4 MPa, the

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(a)

(b)

(c)

(d)

(e)

Fig. 2 Typical characteristic parameter history of acoustic emission test. (a) Amplitude (Amp). (b) Energy. (c) Rise time (Riset). (d) Duration (Dur). (e) Signal count. CHAN

number of AE signals and the amplitude distribution are uniform, mainly distributed in the 40- to 47-dB range; at 495–720 s, with a corresponding pressure of 0.4–0.6 MPa, the signal amplitude value jumps slightly and is evenly distributed at 45–50 dB. However, in the vicinity of 720 s, the signal amplitude value jumps greatly, and some high-amplitude signals are mainly distributed in the 45- to 88-dB range. At 720–840 s, with the pressure sustained at 0.6 MPa, the number of signals is significantly reduced, the amplitude is decreased, and most of the distribution is below 45 dB. At 840–2280 s, with the load increasing, the signal amplitude begins to jump sharply, a large number of high-amplitude signals appear in density, and the signal amplitude distribution is in the 45- to 100-dB range. The phenomenon that the local source rapidly releases energy to generate transient elastic waves in the material is called acoustic emission, also known as stress

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wave emission. The deformation and crack propagation of materials under stress are important mechanisms in structural failure. This source, which is directly related to the deformation and fracture mechanism, is called the AE source. In recent years, another type of elastic wave source that is not directly related to deformation and fracture mechanisms—such as fluid leakage, friction, impact, and combustion—has become known as another or secondary AE source. The process of deformation and fracture of the material under a tensile load to produce an AE signal is mainly the result of internal dislocation motion of the material. During the elastic strain phase of the material, the dislocations are substantially free of motion, so the AE signal is small during the elastic deformation phase. With the development of deformation, the material enters the stage of plastic deformation, and the dislocations are pulled a lot, correspondingly generating a large number of high-amplitude AE signals. With the development of deformation, part of the dislocation motion is blocked and plugging occurs, corresponding to a decrease in the number of AE signals. As the deformation continues to develop, the dislocation plug is pulled and will continue to produce AE signals. When the material breaks, it releases a huge amount of energy and the amplitude of the AE signal reaches the highest value instantaneously. For materials containing crack defects inside the material, the stress field must be concentrated around the boundary of the crack under a load. The concentration of the stress will cause deformation or even damage to the material at the crack tip. It has been shown that under the condition that the stress field near the crack tip reaches a certain value—that is, in the critical state—the crack will rapidly expand, releasing greater energy and generating a higher-amplitude AE signal. “Subcritical” crack propagation is an extension that occurs before the critical crack propagation occurs. In the subcritical crack propagation process, AE mainly comes from two sound sources: (1) the plastic zone, main cracking, and inclusion delamination; and (2) forward movement of the crack front itself. According to the number and amplitude of AE signals with the load and time, the process of AE testing can be divided into four stages: 0–0.4 MPa, 0.4–0.6 MPa, 0.6 MPa, and 0.6–1.7 MPa. The variation in the amplitude with the load and time reflects the evolution of the material damage and defect expansion of the aluminum alloy container. At the first stage, the load is 0–0.4 MPa, the number of AE signals is greater, the amplitude distribution is uniform, and the value is lower. This stage should maybe small plastic deformation stage of the material. At this stage, the load is small and increases slowly; the container material has a uniform plastic deformation, resulting in a larger number but lower amplitude of AE signals. At the second stage, the load is 0.4–0.6 MPa and the number of stages of the signal is quite equal to the first phase, but the amplitude jumps slightly. When the load reaches 0.6 MPa, some high-amplitude signals appear. At this stage a large plastic deformation occurs with microcrack formation and expansion. With the load increasing, the plastic deformation of the container material reaches a certain extent, the material undergoes a large plastic deformation, and small cracks form and expand. At this stage the AE amplitude, energy, rise time, duration, signal count, and other characteristic parameters differ significantly from those at the first stage; thus, the AE signal is generated

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Table 2 Distribution of the main characteristic parameters at different stages of the test process Stage First Second

Third Fourth

Time (s) 0–495 495– 720 720 720– 840 840– 2280

Amplitude (dB) 40–47 47–49.5

Energy (eU) 0–300 0–5160

Rise time (μs) 0–4000 0–99,999

Duration (μs) 0–10,000 0–99,999

Signal count (n) 0–50 0–1172

47–87 40–78

0–15,938 0–95,600

0–99,999 0–1000

0–99,999 0–20,000

0–15,938 0–2000

47–100

0– 3.59
1010

0–99,999

0–99,999

0–17,099

by a different AE mechanism. At the third stage the pressure is sustained at a load of 0.6 MPa. The load is no longer increased, the number of signals is reduced, and the amplitude distribution is not high. At this stage, under a constant load, there is plastic deformation and microcrack expansion. At the fourth stage the load is 0.6–1.7 MPa, the signal amplitude has jumped greatly, and high-amplitude signals (100 dB) are concentrated in a large number. At this stage there is a significant expansion and fracture process. During this phase of loading, the test container intermittently produces abnormal metal fracture sounds at a high amplitude of 100 dB. Previously, the results of AE monitoring of tensile testing of metal materials showed that these high-amplitude signals (100 dB) are produced only when the metal sample breaks, so at this stage there is a significant expansion of the crack, leading to an intermittent metal fracture process. When the load reaches 1.7 MPa, the aluminum alloy container once again issues a loud metal sound and blasting failure of the container occurs because of lack of strength. Figure 2b–e shows the energy, rise time, duration, and signal count characteristic parameters of the history. These parameter distribution characteristics can also be divided into the aforementioned four different stages. The distributions of these characteristic parameters are summarized in Table 2. Observing Fig. 2, Tables 1 and 2, it can be seen that there are significant differences in the characteristics of the AE signals at different stages; most of the AE signals generated at the first stage (the smaller plastic deformation stage) have a shorter rise time and duration, with a low amplitude and only a low signal count. At the second stage (the larger plastic deformation and microcrack formation and expansion stage) the signal rise time and duration are longer, the amplitude and energy are lower, and the signal count is low. The third stage (the stability of crack propagation stage) the signal duration is longer, the rise time is short, the amplitude is high, the energy is high, and the signal count is low. At the fourth stage (the rapid crack expansion and metal fracture stage) the signal has a long duration, the rise time is short, and the amplitude, energy, and signal count are high.

Acoustic Emission Testing of a Friction Stir Welding Aluminum Alloy. . . Fig. 3 Use of three sensors in the source location technique

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Sensor 3 (X3, Y3)

r2 AE source (X5, Y5) D2 r1 Sensor 2 (X2, Y2)

R D1 1



3

Sensor 1 (X1, Y1)

3.2

Reference line

Location Results and Analysis

Source location technology is an important function of AE testing [14]. In a pressure vessel test, the defect location can be determined according to the location result, and the pressure vessel quality condition is evaluated by analysis of the location signal characteristics. In addition, the application of the location results combined with the loading condition can distinguish the pseudopositioning points generated by the noise signals. The more common AE method used in pressure vessel testing is the planar time difference localization method. This method mainly calculates the position of the AE source on the basis of the time difference in reception of the same signal by sensors at different locations. The principle of plane positioning calculation using three probe arrays is shown in Fig. 3. This method was used in the AE test, and the location results are shown in Fig. 4. In Fig. 3, the available input data are the arrival order and arrival time at the three probes and the two time differences, so the following series of equations can be obtained: Δt 1 V ¼ r 1  R

ð1Þ

Δt 2 V ¼ r 2  R

ð2Þ

R¼

1 D1 2  Δt 1 2 V 2 2 Δt 1 V þ D1 cos ðθ  θ1 Þ

ð3Þ

R¼

1 D2 2  Δt 2 2 V 2 2 Δt 2 V þ D2 cos ðθ3  θÞ

ð4Þ

Equations (3) and (4) are hyperbolic equations. By solving the intersection of the two hyperbolas, the location of the AE source can be calculated.

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(a)

(b)

(c)

(d)

Fig. 4 Acoustic emission location (X-Loc.) results at different loading stages. (a) Location results in the test. (b) Location results at 0–0.6 MPa. (c) Location results at 0.6 MPa. (d) Location results at 0.6–1.7 MPa

It can be seen from Fig. 4 that in this test the location results are mainly composed of two large positioning groups, separately located at both ends of the pressure vessel. At the middle of the container, corresponding to the array of sensors S13– S21, the number of location points is small during the test. The second-largest location group has always existed at different stages of the test, and the final burst position of the container also occurred in the second-largest positioning area, indicating that the location signal is generated from defective parts of the container; this shows the accuracy of the AE detection and positioning results. The positioning signal in the first large positioning group is mainly in the pressure-rising stage, and there is no positioning signal in the pressure-sustaining stage. This implies that the positioning signal is closely related to the loading process and is likely to be a noise signal generated during the rise in pressure. Combined with the AE test site, the load is applied by one inlet line located at the head through the hydraulic test machine. The inlet line position is located near the end of the container near the first positioning group, and the inlet line has a longer portion in the container. Thus, the positioning signal of the first large positioning group is likely to be a pseudolocating point generated during the loading process, where the water impinges on the container wall and the noise signal formed. The different time and spatial distribution of the two large positioning groups are further analyzed. In view of the fact that there are fewer location points at the middle part of the container during the whole experiment, the container is divided into two parts by taking the S20 channel as the boundary, and the number of locations in the

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Fig. 5 Different parts of the pressure vessel acoustic emission location (Loc.) history. (a) Second largest group location history. (b) First large group location history. CHAN

different parts of the container during the test are shown in Fig. 5. It can be seen from Fig. 5a that the second-largest positioning group occurs evenly at the first stage and decreases to half the former value at the second stage. At the pressure-sustaining stage, the number of positioning points per unit of time reaches the highest value; the number of location values at the fourth stage is significantly decreased. The signal of the positioning group is mainly caused by the expansion of the defect, and the pressure-sustaining phase is an important stage for analyzing the positioning results of the defect expansion. It can be seen from Fig. 5b that the first large positioning group occurs evenly at the first stage and reaches a maximum at the second stage. At the pressure-sustaining stage the number of positioning points per unit of time is the smallest, and at the fourth stage it gradually decreases to its lowest value. This kind of locating point is mainly formed by a noise signal. It can be seen from the analysis that the locating point formed by water flow impaction is unevenly generated at different load stages and lowest at the pressure-sustaining stage.

3.3

Analysis of Blasting Fracture Morphology

Figure 6 shows the blasting fracture morphology of the aluminum alloy container in this test. The burst has occurred on the weld at the end of the container away from the inlet side, and the burst part is 304 cm away from the flange, with a blast opening width of 25 mm and length of 238 cm. The fracture is mainly located in the array of sensors S25–S33, where the largest opening location is close to the S28 sensor. Through observation of the fracture it can be seen that there are many gaps in the thin metal layer in the fracture inner wall, with an obvious shear lip in the outer wall surface and smooth machining of the groove surface on the middle wall. The weld is only on the internal and external surfaces; the middle part is not penetrated. Therefore, the test container is a typical pressure vessel containing a long incomplete penetration defect. The pressure of the hydrostatic test first yields a fracture in the inner wall. The thickness of the outer wall is small and the strength is insufficient to control the increase in the pressure, resulting in a shear blasting failure. In the AE test process a number of abnormal metal sounds could be heard, suggesting the

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(a)

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(c)

(d)

Fig. 6 Container blasting fracture site. (a) Container blasting fracture. (b) Maximum burst position. (c) Typical blasting fracture site. (d) Near view of blasting fracture site

occurrence of intermittent fractures in the inner wall and the outer wall. The huge energy release during the metal fracture process also results in the high-amplitude (100-dB) and high-energy signals detected by the AE instrument. The occurrence of incomplete penetration defects in FSW is mainly due to improper control of the welding process, resulting in offset of the mixing needle. Because of the nature of such defects, it is difficult for conventional nondestructive testing to achieve satisfactory results. For example, the test containers were tested by application of x-ray and phased-array ultrasound methods before the AE test, but no excessive defects were found. With the application of AE detection technology it is easier to obtain an accurate characterization of the defect expansion process and precise positioning, which shows the sensitivity of AE technology for the detection of incomplete penetration defects in FSW.

4 Conclusion Acoustic emission (AE) technology is a powerful tool for monitoring the expansion of incomplete penetration defects in friction stir welding (FSW) aluminum alloy pressure vessels. The AE characteristic parameters can better characterize unwelded

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defects of FSW aluminum alloy containers with the load expansion process, and the amplitude and signal count characteristic parameters are the most suitable ones. The application of the plane time difference method in the process of AE detection can better realize the positioning of the incompletely welded defects of FSW aluminum alloy containers. AE technology can monitor the damage process of FSW aluminum alloy containers as a whole and provide safety warnings in time before a pressure vessel bursts. The results of this study have relevance for quality inspection of such containers and the detection of service equipment. Acknowledgements The authors thank the Jiangsu Ankao Company for providing the friction stir welded aluminum alloy pressure vessel used in this study, and for their related cooperation.

References 1. X. Jiang-ming, K. Li-ming, X. Ii, Nondestructive evaluation for friction stir welding. Nondestruct Test 30(12), 934–945 (2008) 2. Z. Zhong-Zheng, G. Jian-Ming, H. Liang, The application of acoustic emission technology on coke tower inspection. Nondestruct Test 32(2), 143–148 (2010) 3. J.-R. Kwon, G.-J. Lyu, T.-H. Lee, J.-Y. Kim, Acoustic emission testing of repaired storage tank. Int J Press Vess Pip 78, 373–378 (2001) 4. F. Hong-wei, Y. Ya-lin, L. Sheng-hua, Acoustic emission testing on 1000 m3 LPG pressure vessel. Press Vess Tech 22(5), 56–58 (2005) 5. G.-t. Shen, Z. Yu-feng, D. Qing, Acoustic emission sources from field test of pressure vessels. Nondestruct Test 21(7), 321–325 (1999) 6. V. Smanio, M. Fregonese, J. Kittel, T. Cassagne, F. Ropital, B. Normand, Contribution of acoustic emission to the understanding of sulfide stress cracking of low alloy steels. Corros Sci 53, 3942–3949 (2011) 7. M.G. Alvarez, P. Lapitz, J. Ruzzante, Analysis of acoustic emission signals generated from SCC propagation. Corros Sci 55, 5–9 (2012) 8. C. Jomdecha, A. Prateepasen, P. Kaewtrakulpong, Study on source location using an acoustic emission system for various corrosion types. NDT&E Int 40, 584–593 (2007) 9. M.G. Alvarez, P. Lapitz, J. Ruzzante, AE response of type 304 stainless steel during stress corrosion crack propagation. Corros Sci 50, 3382–3388 (2008) 10. F. Bellenger, H. Mazille, H. Idrissi, Use of acoustic emission technique for the early detection of aluminum alloy exfoliation corrosion. NDT&E Int 35, 385–392 (2002) 11. O.Y. Andreykiv, M.V. Lysak, O.M. Serhiyenko, V.R. Skalsky, Analysis of acoustic emission caused by internal cracks. Eng Fract Mech 68, 1317–1333 (2000) 12. C. Jirarungsatian, A. Prateepasen, Pitting and uniform corrosion source recognition using acoustic emission parameters. Corros Sci 52, 187–197 (2010) 13. J. Jiao, C. He, B. Wu, R. Fei, X. Wang, Application of wavelet transform on modal acoustic emission source location in thin plates with one sensor. Int J Press Vess Pip 81, 427–431 (2004) 14. G. Rongsheng, G. Shen, L. Shifeng, Acoustic emission signal processing. Nondestruct Test 24 (8), 341–345 (2002)

Part V

Condition Monitoring and Diagnosis

Valve Leakage Analysis in Reciprocating Compressor by Using Acoustic Emission Technique H. Y. Sim, R. Ramli, and A. Saifizul

Abstract Non-intrusive measurement technique, namely, the acoustic emission (AE) technique, is often employed to provide an early detection of valve failures in reciprocating compressor. Past researchers often relate the root-mean-square (RMS) value of AE signal to valve leakages. However, the amount of valve leakage is often unknown. In other words, the degree of valve deterioration to the extent that valve should be replaced is unknown. This study examined the effect of increasing valve impacts on AE signal and flow rate of fluid flowing through the suction valve correspondingly, with the intention to relate AE signal to the mass flow rate of fluid. It involves measurement of both suction mass flow rate and AE signal at different compressor speeds and valve conditions and statistical test to investigate the significance of compressor speed on AE signal and mass flow rate of suction valve. It is believed that the study can help to establish an empirical relationship for AE signal and valve leakage rate, which is helpful in estimating the reduction of compressor performance due to valve leakages. Keywords Acoustic emission · Valve leakage rate · Reciprocating compressor · Analysis of variance

1 Introduction Acoustic emission (AE) technique is often employed by plant personnel to monitor the health condition of machine. This is because AE is a direct measurement of damage. The AE sensor measures the transient elastic waves generated during material deformation, rubbing, cracking, impacting, cavitation, and leakage. In reciprocating compressor, valve is often one of the main concerns, as its failures H. Y. Sim · R. Ramli (*) · A. Saifizul Advanced Computational and Applied Mechanics (ACAM) Research Group, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_33

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constituted to a large portion of unscheduled compressor shutdowns [1]. Most valve failures such as incomplete sealing or corrosion of valve plates result in valve leakages, which can eventually affect the efficiency of compressor. Past studies [2, 3] showed that AE technique can detect the abnormalities of valve opening and closing impact. However, the relationship between the AE bursts resulted from the impacts, and valve leakages are less examined. References [4, 5] proposed the degree of valve opening and closing timing and amplitude variation of AE bursts as indicators for valve severity. The authors also found that valve leakages are often accompanied by an increase in the amplitude of continuous AE signal. Nevertheless, the threshold of burst or continuous AE signal and advanced/delayed degree for different valve conditions are not specified. In fact, the relationship between AE signal and leakage of a control valve in a pipeline was investigated by [6, 7]. The authors quantified the valve leakage rate of liquids and gases by AE measurement and other factors such as inlet pressure level, valve size, and valve type, both theoretically and empirically. They further implemented their model into a portable measurement system to detect internal air leakage of valve. Similar study on empirical model of valve leakage was found in [8]. Other approaches such as computation of independent component analysis (ICA) mixing vectors as inputs for regression model can be found in [9]. The flow condition of valves in reciprocating compressor is slightly different as the flow pressure varies with piston position or crank angle. Analytical valve model involves integration of the thermodynamics model and valve dynamics model as the valve movement is caused by the flow of fluid, which is governed by the thermodynamics model [10, 11]. Besides, there are also researchers who coupled valve dynamics with fluid structure interaction approach [12, 13] to simulate valve flow numerically. However, these methods are complex and require high computing power and time. This study intended to find a more direct detection method by obtaining the relationship between valve leakage rate and AE signal empirically. This can be initiated by establishing the theoretical relationship between acoustic power produced by valve impacts and flow velocity.

2 Theoretical Estimation of Valve Flow Rate As the piston moves downward from the top-dead-center (TDC), the lower pressure created in the cylinder causes fluid to flow into the cylinder through suction valve. The sudden introduction of mass into cylinder generates monopole radiation [14]. This is because the oscillating motion of piston creates a fluctuation in the fluid velocity and thus produces a fluctuation of the local pressure of the order of ρv2, according to Bernoulli’s principle. The sound power generated by this monopole source is defined as [15]:

Valve Leakage Analysis in Reciprocating Compressor by Using Acoustic. . .

Ps
ρv2 d3

vv cd

357

ð1Þ

where Ps is the sound power (W), ρ is the fluid density (kg/m3), v is the averaged velocity fluctuation (m/s), d is the size of the region of pressure fluctuation, d3 is small volume element (m3), and c is the sound velocity in the medium (m/s). Equation (1) can be simplified further as: Ps


ρA 4 v c

ð2Þ

where A is the flow area of valve (m2). In this study, as the Mach number is small (0.3), fluid density ρ can be assumed to be almost constant. Besides, as the variation of sound velocity in the medium c and flow area of valve A under different leaked conditions are relatively small, it can be deduced that the sound power Ps is proportional to the fourth power of velocity fluctuation v4, shown as: P s / v4

ð3Þ

The AE signal measured in the study is in the form of u acoustic velocity (m/s). The acoustic velocity and pressure are related by: pðx; t Þ ¼ Zuðx; t Þ

ð4Þ

where p(x,t) is the acoustic pressure (Pa), Z is the specific acoustic impedance (kg/m2 s1), and u(x,t) is the acoustic velocity (m/s). Since u and p are directly proportional, and the square of acoustic pressure p is directly proportional to sound power Ps, it can be deduced that: U 2 / v4

ð5Þ

where U is the RMS value and v is the velocity fluctuation of fluid. As the mass flow rate Qm is a function of fluid velocity v, flow area A, and density ρ, it can be deduced that: U 2 / Q4m

ð6Þ

for the same flow area A and density ρ. By establishing the relationship between RMS value and valve flow rate, the valve leakage rate can be estimated by finding the difference of valve flow rate.

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3 Experiment Setup Experimental setup of this study consists of a single-stage, air-cooled, two-cylinder reciprocating air compressor, modified with a differential pressure meter and a pressure and temperature transducer installed at the suction plenum. The rotational speed of this compressor is varied by a variable frequency drive (VFD) connected to the motor. Figure 1 shows the mounting position of the AE sensor at the suction valve cover. The AE sensor with a frequency range of 100–900 kHz acquires signals from the suction valve cover at a sampling rate of 900 kHz. The signal is acquired together with the pulse generated from the laser tachometer when the piston is positioned at the top-dead-center (TDC). By referring to the pulse, valve impacts observed from AE signal can be analyzed at its corresponding crank angle. The pressure, differential pressure, and temperature are recorded simultaneously. This study is conducted based on the assumption that the rotational speed of compressor is almost constant for each measurement. To simulate different flow rate of valves, the experiment is first conducted with a normal valve plate at increasing compressor speed, starting from 500 to 700 rpm. The experiment is then repeated with an artificially simulated leaky valve plate with a dent of approximately 6 mm diameter. Figure 2 displays the normal and leaky valve plate installed in the suction valve. Each data is acquired at the suction temperature of approximately 30–35  C. A total of 30 samples are acquired for each set of data. To relate the AE signal to the valve impacts, the suction valve opening (SVO) and suction valve closing (SVC) events are identified, as shown in Fig. 3. The RMS values of AE signal are computed at these timing, namely, 30–120 and 150–240 , respectively. Meanwhile, the amount of impacts can also be observed from the mass flow rate of fluid. In this study, the mass flow rate of fluid through suction valve is computed from the differential pressure meter, with formula as follows:

Fig. 1 Experimental test rig with AE sensor mounted on the suction valve cover

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Fig. 2 Test component: (a) suction valve, (b) normal valve plate, (c) leaky valve plate

Fig. 3 Identification of suction valve opening (SVO) and suction valve closing (SVC) events

AE Signal for Normal Suction Valve at 700rpm 3

AE signal/ V

2 1 0 –1 SVC SVO

–2 –3

0

50

100

150

200

250

300

350

Crank angle / degree

π qm ¼ Cε d2 4

sffiffiffiffiffiffiffiffiffiffiffiffiffi 2ρΔP 1  β4

ð7Þ

where C denotes the discharge coefficient of orifice, ε denotes the expansion factor of gases, d denotes the pipe diameter (m), ρ denotes the fluid density (kg/m3), ΔP denotes the suction differential pressure (Pa), and β denotes the ratio of orifice diameter to pipe diameter. Finally, the effect of compressor speeds and valve conditions on the two factors that reveal the valve impacts, namely, RMS value and mass flow rate, is investigated empirically.

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4 Results and Discussions The suction valve opening (SVO) and suction valve closing (SVC) events are found as the sudden bursts of AE signal. As the compressor speed decreases, the amplitude of AE burst decreases. This implies that the valve impacts and the flow rate decrease with speed. Similar findings can be observed in a leaky valve, as displayed in Fig. 4. This happens as a result of lower pressure difference between the suction plenum and cylinder due to leakage, thus producing smaller impacts during the valve opening and closing event. Theoretical derivation showed that the square of RMS value has a direct relationship with the fourth power of flow rate. However, both the RMS value and flow rate of compressor vary with compressor speed and valve conditions. Figure 5a shows that the average RMS value increases with compressor speed at both normal a

AE Signal for Normal Suction Valve at 600rpm 2

b

1.5

1.5 1 AE signal/ V

1 AE signal/ V

AE Signal for Leak Suction Valve at 600rpm 2

0.5 0 –0.5

0.5 0 –0.5

–1

–1

–1.5

–1.5

–2

–2 0

50

100 150 200 250 Crank angle/ degree

300

350

0

50

100 150 200 250 300 Crank angle/ degree

350

Fig. 4 AE signal at 600 rpm: (a) normal valve, (b) leaky valve

Fig. 5 Comparison of average RMS value at various speeds under normal and leaked valve condition: (a) SVO, (b) SVC

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Table 1 Two-way ANOVA test for RMS value at SVO Source Corrected model Intercept Condition Speed Condition * speed Error Total Corrected total a

Type III sum of squares 1.652a 4.546 0.020 1.157 0.083 0.044 9.857 1.696

df 5 1 1 2 2 174 180 179

Mean square 0.330 4.546 0.020 0.578 0.041 0.000

F 1306.163 17,972.347 77.701 2286.444 163.383

Sig. 0.000 0.000 0.000 0.000 0.000

R squared ¼ 0.974 (adjusted R squared ¼ 0.973)

and leaky valve conditions. It can be seen clearly that the effect of speed on RMS value is much lesser at lower speed and increases at higher speed. This may be due to the weak valve opening impact at lower speed, which can be covered by the background noises easily, thus causes less difference between the normal and leaky valve condition. It is suggested to denoise the AE signals, especially for signal at lower speed before performing any analysis. To examine the significance of speeds and valve conditions on RMS value computed at the SVO, two-way ANOVA is performed. Table 1 shows that overall there is a significant effect of both compressor speeds and valve conditions on RMS value at SVO, with F (2, 174) ¼ 2286.444, p < 0.01, and F (1, 174) ¼ 77.701, p < 0.01, respectively. There is also significant interaction between speeds and valve conditions on RMS value at SVO, with F (2, 174) ¼ 163.83, p < 0.01. On the other hand, the two-way ANOVA results for SVC showed significant effect of valve conditions on RMS value computed at SVC, with F (1, 174) ¼ 25.094, p < 0.01. However, the effect of speeds on RMS value at SVC is less significant, with F (2, 174) ¼ 4.642, p > 0.01, causes the interaction of valve conditions and speeds to be less significant, with F (2, 174) ¼ 3.305, p > 0.01. From Fig. 5b, it can be deduced that the speed has less effect on RMS value under normal valve condition. For leaky valve condition, the RMS value decreases with increasing speed. The result suggests that the leaky valve plate causes the impact of suction valve closing decreases at higher speed. For mass flow rate, it was found that both valve conditions and compressor speeds have significant effect, with F (1, 174) ¼ 190.172, p < 0.01, and F (2, 174) ¼ 15,145.859, p < 0.01, respectively. Figure 6 shows that the mass flow rate of both conditions increases with speed; this agrees well with the theoretical model. To relate the RMS value to mass flow rate at varying speed, the trend of both parameters with speed must be similar. By comparing the statistical results of SVO and SVC, it was found that the RMS value computed at SVO is a better indicator for mass flow rate, as both parameters monotonically increase with compressor speeds.

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Fig. 6 Average mass flow rate under normal and leaky valve condition

5 Conclusions This study proposed to obtain the leakage rate of suction valve by establishing an empirical relationship between RMS value and valve flow rate. There are two valve events that are related to the suction valve movement, namely, SVO and SVC event. Results from statistical tests showed that both RMS value at SVO and mass flow rate are significantly affected by the compressor speeds and valve conditions. Besides, both parameters have a positive relationship with compressor speed. This supports the theoretical derivation that the square of RMS value is proportional to the fourth power of mass flow rate. This study confirms that the empirical valve model can be established by finding the relationship of RMS value at SVO and mass flow rate. Acknowledgment The authors would like to thank the Ministry of Science, Technology, and Innovation of Malaysia (Project no. 03-01-03-SF1033) and Institute of Research Management and Monitoring (IPPP) from University of Malaya, Malaysia (Project no. PG233-2014B) for their financial support.

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References 1. D. Goebel, Reciprocating compressor suction and discharge valve monitoring, Prognost Systems GmbH Technical Note, 2013 2. J.D. Gill, E.R. Brown, R.L. Reuben, P.M. Sandford, J.A. Steel, Monitoring of a large reciprocating compressor, in Proceedings of COMADEM 1998, Monash University, Australia, 1998, pp. 317–326 3. M. Elhaj, F. Gu, J. Wright, A.D. Ball, Early detection of leakage in reciprocating compressor valves using vibration and acoustic CWT features, in Proceedings of the 14th International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM), Manchester, UK, 2001, pp. 749–756 4. Y. Wang, A. Gao, S. Zheng, X. Peng, Experimental investigation of the fault diagnosis of typical faults in reciprocating compressor valves. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 230, 2285–2299 (2016) 5. Y. Wang, C. Xue, X. Jia, X. Peng, Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion. Mech. Syst. Sig. Process. 56-57, 197–212 (2015) 6. W. Kaewwaewnoi, A. Prateepasen, P. Kaewtrakulpong, Investigation of the relationship between internal fluid leakage through a valve and the acoustic emission generated from the leakage. Measurement 43(2), 274–282 (2010) 7. A. Prateepasen, W. Kaewwaewnoi, P. Kaewtrakulpong, Smart portable noninvasive instrument for detection of internal air leakage of a valve using acoustic emission signals. Measurement 44, 378–384 (2011) 8. Q. Gao, L. Li, H. Rao, J. Yang, Y. Zhu, Acoustic emission theory and testing technology for quantitative diagnosis of valve leakages. J. Chin. Soc. Power Eng. 32(1), 42–46 (2012) 9. E. Meland, N.F. Thornhill, E. Lunde, M. Rasmussen, Quantification of valve leakage rates. AIChE J. 58(4), 1181–1193 (2012) 10. S. Sun, T. Ren, New method of thermodynamic computation for a reciprocating compressor: computer simulation of working process. Int. J. Mech. Sci. 37(4), 343–353 (1995) 11. J.R. Kolodziej, C.J. Guerra, A validated system-level thermodynamic model of a reciprocating compressor with application to valve condition monitoring, in Proceedings of the ASME Dynamic Systems and Control Conference, California, USA, October, 2013 12. Y. Wang, J. Feng, B. Zhang, X. Peng, Modeling the valve dynamics in a reciprocating compressor based on two-dimensional computational fluid dynamic numerical simulation. Proc. Inst. Mech. Eng. Part E. J. Process Mech. Eng. 227(4), 295–308 (2012) 13. D. Ninković, D. Taranović, S. Milojević, R. Pešić, Modelling Valve Dynamics and Flow in Reciprocating Compressors, Internet sources from http://oaji.net/articles/2014/7661398015561.pdf 14. M.G. Nored, G. Tweten, K. Brun, Compressor Station Piping Noise: Noise Mechanisms and Prediction Methods, Interim Report, Gas Machinery Research Council, Southwest Research Institute 15. P.M. Morse, K.U. Ingard, Theoretical Acoustics (McGraw-Hill, Inc., New York, NY, 1968)

Determination of Characteristic Frequency Segments of Acoustic Emission Signal for Valve Leakage Detection in Reciprocating Compressor R. Ramli, H. Y. Sim, and A. Saifizul

Abstract Acoustic emission (AE) technique is often employed by condition monitoring personnel to detect abnormalities in moving components of machines. Due to its low signal-to-noise ratio at high-frequency range, it often requires certain signal processing technique to extract valuable information from machine parts. This study intends to obtain the characteristic frequency segments of AE signals that correspond to the physical movement of valves in reciprocating compressor. It involves acquisition of AE signals at various simulated valve conditions and rotating speeds, decomposition of these signals through wavelet packet transform (WPT), and computation of crest factor (CF) of WPT coefficients at a specific crank angle. The characteristic frequency segments that indicate valve problems are often accompanied by an extra high or low CF value. By relating the CF value to valve events, the condition of valve can be predicted. It is hoped that this study can provide a methodology to obtain valve information effectively and efficiently while reducing unwanted and overloading information of AE signals. Keywords Acoustic emission · Valve leakage · Reciprocating compressor · Wavelet packet transform

1 Introduction Acoustic emission (AE) technique measures the transient elastic waves generated due to deformation or dislocation within a material. Interactions between two materials, namely, impact, cavitation, friction, and leakage, are also sources of AE signals. Most AE signals have frequency ranged between 100 kHz and 4 MHz. These high-frequency AE signals attenuate faster and thus often buried under less R. Ramli (*) · H. Y. Sim · A. Saifizul Advanced Computational and Applied Mechanics (ACAM) Research Group, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_34

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attenuated, low-frequency machinery noise. In addition, material type, surface coupling of AE sensor, mounting position of AE sensor relative to source, and electromagnetic interference are some of the other factors that add noises to the AE signal. To extract time variant features of faulty machinery components from these strong background noises, time-frequency analysis technique such as the wavelet transform was employed widely in machinery monitoring. Numerous studies showed the application of wavelet transform in leak detection of pressure vessels [1], pipelines [2, 3], gear failures [4], bearings [5], valves of reciprocating compressor [6–8], and internal combustion engine [9, 10]. This study intended to obtain the characteristic frequency segments that best depicting the fault features of valve leakage in reciprocating compressor. Although Fourier transform can give better precision in the frequency domain, it requires signal to be stationary, and it does not provide information in time domain. As the AE signal acquired contains information of valve opening and closing impacts that are mostly transient in nature, signal processing technique such as wavelet packet transform that has good localization property is desirable because it can detect abrupt changes in signal [11]. The good temporal property of wavelet transform is given by the shortwave nature of its basis function.

2 Wavelet Packet Transform (WPT) While Fourier transform computes the integral of the product of AE signal and a windowing function, wavelet transform involves similar computation with scaled wavelet function shifted across the whole time frame. The portion of AE signal which has similar shape as the scaled wavelet function will show higher value of wavelet coefficient. In wavelet packet transform (WPT), it involves the decomposition of both approximation and detail space, i.e., low- and high-frequency space. The wavelet packet function W nj, k ðt Þ is initiated by the scaling function ϕ(t) and the mother wavelet function, ψ(t) as displayed in Eqs. (1) and (2): W 00,0 ðt Þ ¼ ϕðt Þ

ð1Þ

W 10,0 ðt Þ ¼ ψ ðt Þ

ð2Þ

where n, j, and k represent the modulation parameter, scale parameter, and translation parameter, respectively. Further decomposition for n ¼ 2, 3, 4, . . . can be computed by recursive relationship in Eqs. (3) and (4):

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Level 2

Level 1

Level 0

h(n)

S

2

0 1, k

S 00,k

g(n)

S 11,k

2

h(n)

2

S 20,k

g(n)

2

S 21,k

h(n)

2

S 22,k

g(n)

2

S 23,k

Fig. 1 WPT decomposition with successive filtering and downsampling

W 2n 0,0 ðt Þ ¼

pffiffiffi X 2 hðkÞW 1n, k ð2t  k Þ

ð3Þ

k

W 2nþ1 0,0 ðt Þ ¼

pffiffiffi X 2 gðkÞW 1n, k ð2t  k Þ

ð4Þ

k

where h(k) and g(k) are the low-pass and high-pass filters, respectively, associated with the predefined scaling function and mother wavelet function. As the scale level j increases, the bandwidth is halved for every level, while the time resolution increases inversely. Eventually, the wavelet packet coefficient w nj, k is computed as Eq. (5): w nj, k

¼

D

f ðt Þ; W nj, k

E

ð ¼ f ðt ÞW nj, k ðt Þdt

ð5Þ

In the present study, the raw AE signal of one revolution S00, k is acquired and decomposed into three resolution levels. The concept of WPT decomposition is displayed in Fig. 1. Signal S12, k represents second subspace in the second resolution level. There are a few requirements need to be fulfilled for a function to be chosen as the mother wavelet. Firstly, the function must satisfy the admissibility condition in Eq. (6) to ensure no information loss during signal analysis and signal reconstruction. This implies that the Fourier transform of wavelet function is zero at zero frequency, thus enabling the function to have a band-pass spectrum characteristic [12]. Moreover, zero at zero frequency also means that the average value of the function in the time domain is zero; therefore the function must be oscillatory. In addition, the regularity condition which ensures a smooth and fast-decay wavelet function and the orthogonality condition that enables a non-overlapped signal in the wavelet space need to be satisfied for selection of mother wavelet function.

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Fig. 2 Decomposition (a) scaling and (b) wavelet function for biorthogonal 3.3 wavelet þ1 ð

Cψ ¼ 1

jψ^ ðωÞj2 dω < 1 j ωj

ð6Þ

The biorthogonal wavelet is chosen as the mother wavelet function in this study as it satisfies all the conditions stated above, with signal orthogonal to its dual space. Besides, it has a good linearity in phase which reduces phase distortion during reconstruction while providing good time-frequency localization at higher order. Figure 2 shows the decomposition of biorthogonal scaling function and wavelet function employed in this study.

3 Methodology 3.1

Experiment Setup

Experimental setup of this study consists of a single-stage, air-cooled, two-cylinder reciprocating air compressor connected to a variable frequency drive (VFD), as displayed in Fig. 3. The experiment is conducted at motor speed of 600 and 800 rpm by adjusting the input frequency of motor through VFD. This study intended to examine the impacts between suction valve plate and valve guard caused by compressed air at a specific crank angle of reciprocating compressor. A wideband AE sensor with operating range of 100–900 kHz is mounted on the suction valve cover of test compressor. The sensor is connected to a coupler for signal conditioning and then to a high sampling rate digitizer for acquisition at 1 MHz. The signal is acquired simultaneously with the pulses generated by a laser tachometer directed to the flywheel, where the high pulse represents piston position at the top-dead-center (TDC). By referring to the pulse from tachometer, valve

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Fig. 3 Experimental test rig with (a) AE sensor mounted on valve cover (b) reflective tape for tachometer (c) suction valve

Fig. 4 Suction valve plate at (a) normal (b) single leak (c) double leak condition

events observed from AE signal can be analyzed at its corresponding crank angle. This study is conducted based on the assumption that the rotational speed of compressor is constant for each measurement. To simulate leakage in the suction valve, the valve plate is ground with a dent of 6 mm diameter. The severity of valve leakage can be emulated by increasing the number of dents on the valve plate. Figure 4b shows the single leak valve plate with one dent, while Fig. 4c shows two dents on the double leak valve plate.

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Fig. 5 Segregation of AE signal into different time segments

T1

3.2

T2

T3

T4

Data Preparation

The experiment is conducted at suction valve temperature of approximately 40–50  C and atmospheric pressure. To correlate the AE signal with the suction and discharge valve timing, the signal acquired is further segregated into four different time segments, namely, discharge valve closing (T1), suction valve opening (T2), suction valve closing (T3), and discharge valve opening (T4). Figure 5 shows the segregation of one revolution of AE signal into T1 (0–30 ), T2 (30–120 ), T3 (150–240 ), and T4 (270–330 ). The boundaries of these four time segments are selected by referring to the work from [13], where the authors compared the burst AE signal with the actual valve motion measured by the eddy current displacement sensor. With the compressor speed of 970 rpm, the discharge valve closing, suction valve opening, suction valve closing, and discharge valve opening event are found to begin at 0 , 30 , 180 , and 290 , respectively [13]. In this study, we decrease the lower boundary of T3 and T4 to 150 and 270 compared to [13] to accommodate earlier suction valve closing and discharge valve opening event resulted from valve leakages. Meanwhile, as most signals from the range of 120–150 and 240–270 show consistent random noises, these two ranges are discarded for analysis. Raw AE signals acquired are filtered through third resolution level of WPT decomposition algorithm before signal segregation. Eight subspaces are produced from each WPT decompositions, where each of them corresponds to a particular frequency range, namely, F1 (0–62.5 kHz), F2 (62.5–125 kHz), F3 (125–187.5 kHz), F4 (187.5–250 kHz), F5 (250–312.5 kHz), F6 (312.5–375 kHz), F7 (375–437.5 kHz), and F8 (437.5–500 kHz). Thus, 32 time-frequency segments are generated for each AE sample. As these time-frequency segments depict valve impacts, crest factor (CF) is selected as condition indicator as it is sensitive to sudden pulse rise. Equation (7) shows the computation of CF from WPT coefficients,

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where A denotes the highest amplitude, while μ denotes the root-mean-square (rms) value of AE signal. A total of 20 samples are acquired for each set of data under three valve conditions and two rotational speeds. C¼

A μ

ð7Þ

4 Results and Discussions It can be seen from the AE signals acquired that the signal-to-noise ratio decreases as the severity of valve leakage increases from single leak to double leak, as shown in Fig. 6. The background noises may increase further at lower compressor speed. For results in 800 rpm, the CF value is highest in T3 (150–240 ) for all frequency ranges under normal valve condition. Meanwhile, for single leak valve condition, the CF value is highest in T3 (150–240 ) for F1–F6 (0–375 kHz) and highest in T2 (30–120 ) for F7 (375–437.5 kHz) and F8 (437.5–500 kHz). The CF value for double leak valve condition is quite consistent, as it records highest value at T4 (270–330 ) for all frequency ranges. In fact, a high CF value is caused by the high peak value and low background noises (low rms value) of the AE signal. Thus, it can be observed from Fig. 6 that the peaks in T3 (150–240 ) for normal valve condition, T2 (30–120 ) for single leak valve condition, and T4 (270–330 ) for double leak valve condition are distinct and sharp compared to other peaks. The highest averaged CF value in each time segments and valve conditions, together with its corresponding frequency segment, is identified and grouped in Table 1. It can be observed from the table that most of the characteristic frequency segments are high-frequency segments (F4 and above, more than 250 kHz), except T1 and T4 under single leak valve condition. The low characteristic frequency segments, F2 (62.5–125 kHz) and F1 (0–62.5 kHz) for T1 (discharge valve closing) and T4 (discharge valve opening), are due to the fact that these impacts are mostly mechanical impacts from neighboring discharge valve, hence falls in the lower-

Fig. 6 Raw AE signal acquired at (a) normal (b) single leak (c) double leak valve condition at 800 rpm

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Table 1 Averaged crest factor of AE signal acquired at 800 rpm under different valve conditions and time segments Valve condition Time segment Crest factor Frequency range

Normal T1 T2

T3

T4

Single leak T1 T2 T3

T4

Double leak T1 T2 T3

T4

6.29 F5

9.56 F8

6.27 F4

5.84 F2

6.47 F1

6.15 F8

8.31 F6

(a)

6.61 F5

(b)

9.2 F8

7.46 F6

5.44 F6

6.15 F5

(c)

Fig. 7 WPT AE coefficients computed for (a) normal (b) single leak (c) double leak valve condition at 800 rpm

frequency range, as most high-frequency components of discharge valve are attenuated before reaching the suction valve. On the other hand, double leak valve condition shows high-frequency segments, F6 (312.5–375 kHz) at T4 (discharge valve opening). This may happen as a result of backflow of fluid from suction valve into cylinder, therefore increasing the amount of compressed fluid flowing through discharge valve, producing higher impact during discharge valve opening. Figure 7 shows the WPT coefficients of AE signals at normal, single leak, and double leak valve conditions. The impacts of these signals look more distinct compared to the raw AE signal in Fig. 6. As the compressor speed decreases from 800 to 600 rpm, the impacts of both valves decrease, causing them to be more prone to bury under much stronger background noises. After WPT decomposition, the CF is found to record the highest value in T3 (150–240 ) under normal valve condition and T2 (30–120 ) under single leak valve condition, for all frequency ranges. Compared to normal valve, the impact in T3 (suction valve closing) is less obvious for single leak valve as it has lesser impact due to the leaked valve plate. Meanwhile, double leak valve showed highest CF value in T4 (270–330 ) for F1–F3 (0–187.5 kHz) and highest in T3 (150–240 ) for F5–F8 (250–500 kHz). Similar to 800 rpm, the low-frequency mechanical impacts from neighboring valve (T4) are shown clearer in lower-frequency range. Table 2 shows the highest averaged CF value and its corresponding frequency segment at each time segments and valve conditions. It can be seen that most of the characteristic frequency segments are high-frequency segments, similar to the results in 800 rpm. However, if we inspect the whole AE signal after WPT decomposition, it

Valve condition Time segment Crest factor Frequency range

Normal T1 6.17 F5

T2 7.40 F8

T3 13.80 F8

T4 6.97 F5

Single leak T1 T2 5.49 7.44 F2 F5 T3 6.90 F3

T4 6.82 F3

Table 2 Averaged crest factor of AE signal acquired at 600 rpm under different valve conditions and time segments Double leak T1 T2 5.85 5.39 F2 F7

T3 6.16 F8

T4 5.97 F2

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(a)

(c)

(b)

(d)

Fig. 8 AE signals for single leak valve at 600 rpm for (a) raw AE signal (b) WPT coefficients at F2 (c) WPT coefficients at F5 (d) WPT coefficients at F3

was found that F2 (62.5–125 kHz) is the best characteristic frequency segments compared to others. Figure 8c shows the WPT decomposed single leak signal for highest averaged CF value for T2 (30–120 ) at F5 (250–312.5 kHz), while Fig. 8d displays the WPT decomposed single leak signal for highest averaged CF value for T3 (150–240 ) and T4 (270–330 ) at F3 (125–187.5 kHz). Despite having higher CF value compared to F2 (62.5–125 kHz), signals in Fig. 8c, d are found to have a much lower signal-to-noise ratio. As valve plate deteriorates further, the valve impact is further reduced, causes most peaks to bury under background noises, as shown in Fig. 9a for raw AE signal under double leak valve condition. Although the high-frequency segments (F7 and F8) showed highest averaged CF value, as displayed in Table 2, these impacts are indiscernible from the background noises, as displayed in Fig. 9c, d. In fact, it can be seen from Figs. 8b and 9b that low-frequency segment, F2 (62.5–125 kHz), gives a better representation of the valve condition. With higher signal-to-noise ratio, the features of faulty valve can be observed more clearly. Figure 10c reveals an early suction valve closing at approximately 150 with double impacts under double leak valve condition at 600 rpm instead of a single impact at approximately 180 at the

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Fig. 10 Comparison of valve event timing for (a) normal (b) single leak (c) double leak valve condition at 600 rpm

same speed under normal condition. Suction valve closes when the pressure inside the cylinder is higher than the suction plenum pressure. With double leaked valve plate, the pressure difference between cylinder and suction plenum decreases, resulting in an earlier occurrence of valve closing event. In addition, there is a slight delay of approximately 17 in discharge valve opening for double leak valve. As suction valve closes earlier, the mass of fluid flowing into the cylinder is further

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reduced. Therefore, piston needs to compress the fluid further to achieve pressure required for discharge valve opening. Similar trend can be observed for the single leak valve in Fig. 10b.

5 Conclusions It is a common belief that extra high sampling frequency is needed for acquisition of AE signals, especially when the background noises are high. This study intended to obtain the characteristic frequency segments of different valve conditions at 600 and 800 rpm through WPT decomposition. It was found that CF shows a higher value in higher frequency range (250 kHz and above) for all three valve conditions at 800 rpm, as higher speed produces greater impacts, resulting in significant increment in the peak amplitude compared to the background noises. Thus, F5 (250–312.5 kHz) can be the characteristic frequency segments for valves operating at 800 rpm. As compressor speed decreases to 600 rpm, the characteristic frequency segments shifted to a lower-frequency range at F2 (62.5–125 kHz), due to the reduction of valve impacts at lower speed. Although CF showed higher value in the high-frequency segments, valve events are shown poorly in these frequency segments. Thus, CF cannot be the only condition indicator for valve failure detection, especially when the impacts are weak or the background noises are high. It would be a good practice to check the shape and coefficients of WPT decomposed signal first before deciding the characteristic frequency segments. Determination of characteristic frequency segments, especially at the low compressor speed, can reduce the sampling frequency, thus resolving the issue of large volume of AE data during acquisition and analysis. Acknowledgment The authors would like to thank the Ministry of Science, Technology, and Innovation of Malaysia (Project no.: 03-01-03-SF1033/SF005-2015) and Institute of Research Management and Monitoring (IPPP) from University of Malaya, Malaysia (Project no.: PG2332014B) for their financial support.

References 1. S.V. Subba Rao, B. Subramanyam, Analysis of acoustic emission signals using wavelet transformation technique. Def. Sci. J. 58(4), 559–564 (July 2008) 2. R. Wu, Z. Liao, L. Zhao, X. Kong, Wavelet application on acoustic emission signal detection in pipeline, in Canadian Conference on Electrical and Computer Engineering, Canada, May 2008, pp. 001211–001214 3. K. Yoshida, H. Kawano, Y. Akematsu, H. Nishino, Frequency characteristics of acoustic emission waveforms during gas leak, in Proceedings of European Working Group on Acoustic Emission (EWGAE), 2004, pp. 321–327 4. N. Baydar, A. Ball, Detection of gear failures via vibration and acoustic signals using wavelet transform. Mech. Syst. Signal Process. 17(4), 787–804 (2003)

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5. J. Rafiee, M.A. Rafiee, P.W. Tse, Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Exp. Syst. Appl. 37(6), 4568–4579 (2010) 6. M. Elhaj, F. Gu, J. Wright, A.D. Ball, Early detection of leakage in reciprocating compressor valves using vibration and acoustic CWT features, in Proceedings of the 14th International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM), Manchester, UK, 2001, pp. 749–756 7. R.P. Ramesh, Compressor valve Failure Detection and Prognostics, Master of Science Thesis, University of Missouri-Rolla, 2007 8. H.Y. Sim, R. Ramli, M.A.K. Abdullah, Implementing wavelet packet transform for valve failure detection using vibration and acoustic emission signals. J. Phys. 364(012086), 1–14 (2012) 9. J.D. Wu, C.H. Liu, An expert system for fault diagnosis in internal combustion engine using wavelet packet transform and neural network. Exp. Syst. Appl. 36, 4278–4286 (2009) 10. A.K. Frances, J.D. Jill, R.L. Reuben, J.A. Steel, Investigation into identification of faults in a small HSDI diesel engine using acoustic emission, in Proceedings of European Working Group on Acoustic Emission (EWGAE), 2004, pp. 311–320 11. E.P. Serrano, M.A. Fabio, Application of the wavelet transform to acoustic emission signals processing. IEEE Trans. Signal Process. 44(5), 1270–1275 (May 1996) 12. C. Valens, A really friendly guide to wavelets, Internet sources from [email protected], 1999, pp. 1–19 13. Y. Wang, C. Xue, X. Jia, X. Peng, Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion. Mech. Syst. Signal Process. 56-57, 197–212 (2015)

Acoustic Emission Fault Diagnosis of Rolling Bearing Based on Discrete Hidden Markov Model Fuping Guo, Shuqian Shen, Zhihong Duan, Zhiqing Fan, and Zhiwei Sun

Abstract Acoustice emission (AE) technology has emerged as a promising diagnostic approach for rolling bearing fault detection. In this paper, the discrete hidden Markov chain model (DHMM) is used to diagnose faults based on AE signals. A tool built by MATLAB software is used to collect the acoustic emission signals of the rolling bearings for data reading and frame processing and then extract the vector that reflects the characteristics of the rolling bearing. The feature vectors are analyzed and diagnosed by using the DHMM. The results show that the DHMM method can provide reliable fault diagnosis for a rolling bearing. Keywords Rolling bearing · Acoustic emission · Markov model · Framing · Wavelet packet decomposition · Fault diagnosis

1 Introduction Rotating machinery is one of key equipments in various sectors of all industries. The bearing is one of the most widely used and consumable parts in rotating machinery applications. Because of long-term rotation of rotating machinery, frequency of faults is high, and sometimes a small scale fault may cause a chain reaction resulting in casualties and huge economic losses [1–4]. So diagnosis of rolling bearing failure become very important to secure process safety. The acoustic emission (AE) is a kind of dynamic nondestructive testing technology which can detect energy changes from dedecting objective, so that it can be used to diagnose the rotating machinery faults [5, 6]. It is critical to extract characteristic signals for fault diagnosis of rolling bearing based on acoustic emission signal [7–9]. A explicit relationship between the

Mainly engaged in acoustic emission detection technology and fault diagnosis research. F. Guo (*) · S. Shen · Z. Duan · Z. Fan · Z. Sun College of Mechanical and Electrical Engineering, Guangdong University of Petrochemical Technology, Maoming, China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_35

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Fig. 1 Rolling bearing test rig

characteristic singals and the rolling bearing faults is unclear, and the discrete hidden Markov model (DHMM) is developed to represent the relationship. The reason to use the DHMM is that its hidden states can be used to model the inexplicit relationship, and this feature has been widely implemented in many [10–12]. Artificial defects on the outer ring and the inner ring are set up for rolling bearing fault diagnosis; the data analysis and DHMM built are conducted by MATLAB codes. The paper is organized as follows: the experimental device is discussed in Sect. 2, it follows an introduction of theory and methods, the main results are discussed in Sect. 4, and finally conclusions about this paper are summarized.

2 Experimental Device The rolling bearing test platform is shown in Fig. 1; acoustic emission signals (labeled as sensor in Fig. 1) are collected by the American Physical Acoustics Corporation (PAC) production of PCI-2 all-digital acoustic emission acquisition system, the resonant frequency of about 0–1000 kHz WD broadband sensor, 2/4/6 preamplifier. The NSK1310 aligning ball series of rolling bearings are used for testing; two artificial faults on outer ring wear rolling bearings and inner ring wear rolling bearings are used for this study.

3 Basic Theory and Process 3.1

DHMM Basic Theory

The hidden Markov model (HMM) is based on the Markov model which includes a number of states and transition probabilities from state to state. A HMM has a simple Markov model as its core, but only observation signals are used instead of the states. This model structure is suitable to represent an actual problem or relation having

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more complex states which are not easily measured. The HMM includes a double random process: one is the Markov chain which is a basic stochastic process that describes the transition between states; another one is a stochastic process describing statistical correspondence between states and the observed variables (or observations). The HMM can be divided into continuous HMM (CHMM) and discrete HMM (DHMM). The observed sequence of DHMM is within a range of discrete numbers, and observations can be represented by symbols or codes. In this study, DHMM method will be used for rolling bearing fault diagnosis [13–16].

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Ascoustic Emission Signal and DHMM Implementation

Subframe Processing of Acoustic Emission Signals Before the eigenvector of AE signals is extracted, they must firstly be framed by multiple time intervals. The time-domain waveform signals of the AE sampling signals for the rolling bearing are framed as shown in Fig. 2. The time-domain waveform signal of the rolling bearing is divided into T segments with the same length, each segment representing one frame, so that the sampling sequence in each state can be divided into individual short sequences.

Acoustic Emission Signal Wavelet Packet Eigenvector Extraction The premise of fault diagnosis of rolling bearing based on hidden Markov model is to extract the eigenvector of acoustic emission signals. The method of wavelet packet energy analysis is used to extract the eigenvector of acoustic emission signal. The three-level wavelet packet decomposition is carried out after the framing of the AE signals, and the energy feature of each band of each frame signal in fault states is extracted, and eigenvector can be calculated. Table 1 shows partial results of

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Table 1 Decomposition energy feature extraction results for first frame signals Sample Normal status

Outer ring failure

Inner ring failure

Serial number 1 2 3 1 2 3 1 2 3

Part of the eigenvector spacing 0.8647 0.2184 0.2037 0.8643 0.2188 0.2029 0.8640 0.2193 0.2038 0.8617 0.2220 0.2071 0.8628 0.2200 0.2099 0.8622 0.2211 0.2077 0.8589 0.2241 0.2139 0.8590 0.2239 0.2122 0.8591 0.2238 0.2133

0.2504 0.2515 0.2509 0.2557 0.2522 0.2548 0.2574 0.2589 0.2578

the normal state rolling bearing, the inner ring fault rolling bearing, and the outer ring fault rolling bearing from the first frame signals.

DHMM Training for Multiple Observation Sequences In this study, each state of the DHMM is trained using multi-observation samples. The initial model to starting the DHMM training can be randomly selected, but the most important is to revaluate the model parameters. Using the classic HMM training algorithm, B-W algorithm, the DHMM will be established for different failure modes. It is assumed to be left- and right-type structure. Since states have always been incremented in time, the model is at the state 1 at time, t ¼ 1, while the model at the state N at time t ¼ N, (the numbers of 1, 2, . . ., N represent Markov chain states). However, HMMs cannot use a single observation samples to train the model because transient nature of the model’s internal state allows only less observations to be used for each state. Therefore, it is necessary to use multiple observation sequences to carry out training. Through multi-observation sequence training, best parameters of DHMM can be obtained, so that the joint probability of these sequences reaches maximum. If there are L training samples, after extracting the feature vector and quantizing the vector, there will be L observation sequence set: O ¼ {O(1), O(2),   , O(L )}, where OðlÞ ðlÞ ¼ O1 , O2l ,   , OTl l represents the lth observation samples and Tl denotes the length of the lth observation. Given the set of L observation sequences O and DHMM parameter set λ, we can calculate the probability: PðO=λÞ ¼

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A denotes the state transition probability matrix, and B denotes the observed  B to maximize probability matrix, we can obtain the new parameter set λ ¼ π, A, the joint probability of these sequences. A certain method is used to determine whether it is convergence; if it is no convergent, then it is necessary to re-evaluate another set of parameters. The procedures will stop if the convergence is achieved. After the feature vector of the rolling bearing is extracted, the DHMM training is carried out. The DHMM will be trained in three states/modes: normal rolling bearing, outer ring fault, and inner ring fault. Figure 4 shows the DHMM training curve for the normal state of the rolling bearing at 100 r/min speed. The maximum log likelihood probability can be obtained by training the three states of the rolling bearing DHMM. As the number of iterations increases, the log likelihood probability increases continuously until it reaches a maximum. Table 2 lists the iteration numbers for three modes: normal rolling bearing, outer ring fault, and inner ring fault.

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Table 2 Number of iterations required for training under three modes The state of the bearing Number of iterations

Normal status 17

Outer ring failure 15

Inner ring failure 10

4 Results and Discussion The rolling bearings in the normal state, the outer ring fault rolling bearings, and the inner ring rolling bearings were operated in speed ranges of 100, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 r/min. For each speed, ten samples were collected and numbered as 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. The observed sequence is entered the DHMM; the logarithmic likelihood value in each state of the model is calculated. The best log likelihood probability of each of the ten samples can be obtained.

4.1

Comparison of Logarithmic Likelihood Probability Values of Three Bearing States

It can be seen from Fig. 5 that the logarithmic likelihood probability of the normal state is generally above 210 and the logarithmic likelihood probability values for faults are below than 260. We will take the minimum value of the logarithmic probability range (260 to 210) for the normal state and fault state of the dividing line; this kind selection will reduce fault misjudgment phenomenon.

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DHMM Test

To test our DHMM fault diagnosis, we introduce another fault about the bearing ball. One bearing ball is artificially worn and installed into the test rig shown in Fig. 1. The logarithmic likelihood probability for this fault situation is shown in Fig. 6. Since the logarithmic likelihood value of the rolling bearing failure is less than 260 after the test, it indicates there is a fault in the rolling bearing. From this test, we can conclude that the proposed method can provide reliable fault diagnosis for the rolling bearing using AE signals.

5 Summary In this paper, we implement the DHMM for rolling bearing fault diagnosis using the acoustic emission signals. The DHMM is trained from the frame vector of the acoustic emission signal and the feature vector extracted by the wavelet packet.

Logarithmic likelihood value

Normal Out ring fault Inner ring fault Dividing line

Test sample number Fig. 5 Comparison of results from DHMM

Logarithmic likelihood

Dividing line Rolling ball fault

Test sample number Fig. 6 Rolling bearing body failure DHMM

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The results show that the DHMM is a reliable method for rolling bearing fault diagnosis. The DHMM can effectively determine the running state of rolling bearings, and it can be implemented on site. The DHMM can handle non-smooth rolling bearings with large amount of information, so that it has a good prospect in the fault diagnosis of rolling bearing.

References 1. J. Han, R.L. Zhang, Failure Mechanism and Diagnostic Technology of Rotating Machinery (Machinery Industry Press, Beijing, 1997) 2. B.Y. Wang, Y.Q. Liu, Y.Y. Liao, Sensitivity analysis of rolling bearing fault signal timedomain characteristic index. Bearing 43(10), 45–48 (2015) 3. Y.Z. Liu, X. Zhang, J. Wu, Contact fatigue microcosmic mechanism and influencing factors of rolling bearings. Bearing 43(10), 53–57 (2015) 4. K.S. Andrew, D.L. Jardine, D. banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20, 1483–1510 (2006) 5. R.J. Hao, W.X. Lu, F.L. Zhu, Acoustic emission detection technology used for rolling bearing fault diagnosis research. Vib. Shock 27(3), 75–79 (2008) 6. M.Y. Li, Z.D. Shang, H.C. Cai, Acoustic Emission Detection and Signal Processing (Science Press, Beijing, 2010) 7. F. Qi Minfang, J. Zhongguang, et al., Comprehensive evaluation method of thermal power unit based on information entropy and principal component analysis. J. Elect. Eng. China 33(2), 58–64 (2013) 8. L. Jiang, J.P. Xuan, T.L. Shi, Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis. Mech. Syst. Signal Process. 41, 113–126 (2013) 9. Y.M. Hou, J.B. Sun, Y. Zhang, Fault diagnosis of rolling bearings based on PSO-BP neural network and Hilbert spectrum singular value. Comb. Mach. Tool Auto. Mach. Tech. (7), 77–83 (2014) 10. C.J. Feng, HMM Dynamic Pattern Recognition Theory and Method, and Its Application in Rotating Machinery Fault Diagnosis, PhD thesis, Hangzhou: Zhejiang University, 2002 11. H.F. Yuan, C. Ji, H.Q. Wang, Intelligent diagnosis method for rolling bearing based on GA and DHMM and KPCA-RS improvement research. Measur. Cont. Tech. 33(11), 21–28 (2014) 12. W. Fan, P. Fu, Q.Q. Zheng, Rolling bearing fault diagnosis based on DHMM. Mech. Eng. Auto. 4, 132–135 (2015) 13. Z. Wu, S.X. Yang, A New Method for Fault Feature Extraction and Pattern Classification of Rotating Machinery (Science Press, Beijing, 2012) 14. L.R. Rabiner, A tutorial on models and selected applications in speech recognition. Proc. TEES 77(2), 257–286 (1989) 15. R.J. Elliott, J. Deng, Change point estimation for continuous-time hidden Markov models. Syst. Cont. Lett. 62(2), 112–114 (2013) 16. D.A. Tobon-Mejia, K. Medjaher, N. Zerhouni, A data-driven failure prognostics method based on mixture of Gaussians Hidden Markov Models. IEEE Trans. Reliab. 61(2), 491–503 (2012)

Part VI

Miscellaneous

Influencing Factors of Partial Discharge of Needle-Plate Based on Acoustic Emission Detection Yu Zhang, Longbiao He, and Haijiang Zhu

Abstract Partial discharge (PD) is one of the main factors leading to the failure of power system insulation and malfunction. The study of discharge characteristics is helpful to fault prevention. A needle-plate partial discharge system is established. According to the characteristics of time domain and frequency domain of acoustic emission signal produced by partial discharge, the influence of the tip taper, needleplate spacing, and plate electrode size on the partial discharge is studied. The experimental results show that the discharge frequency and discharge energy of the partial discharge acoustic emission signal are closely related to the electrode taper, the needle-plate spacing, and the size of the plate electrode. When the taper of the needle electrode, needle plate spacing, and the diameter of the plate electrode are increased, the frequency and the energy of the partial discharge acoustic emission signal in unit time under the same voltage level are obviously reduced. The analysis of short-time Fourier transfer of time domain signal shows that the magnitude of partial discharge signal can affect the frequency characteristics. Keywords Acoustic emission · Partial discharge · Needle-plate model · Discharge frequency · Short-time Fourier transform

1 Introduction With the continuous development of the power industry, maintenance and monitoring of power systems become a vital part of the power system. The partial discharge is one of the main factors leading to the failure of the insulation failure of the power system [1]. In the study focusing on partial discharge characteristics, usually there Y. Zhang · H. Zhu Beijing University of Chemical Technology, Beijing, China e-mail: [email protected]; [email protected] L. He (*) National Institute of Metrology, Beijing, China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_36

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are discharge models such as needle plate, plate, void, suspension, and so on. The partial discharge model of needle plate can effectively characterize the partial discharge caused by insulation defects such as metal protrusions inside the power equipment [2]. Most of the researches on the partial discharge of the needle-plate were based on the pulsed current method, which mainly focused on the physical and chemical properties in the partial discharge process or the damage caused by the partial discharge to the insulation system [3], but the pulse current method will be affected by electromagnetic interference, which cannot adapt to online monitoring and other conditions. In the partial discharge process, besides producing the pulsed current, energy released by the partial discharge is propagated in an insulating medium or other structures (also referred to as an acoustic emission phenomenon) in an elastic wave. The elastic wave can also reflect the characteristics of the partial discharge. The partial discharge detection method based on acoustic emission has been widely used in the detection of partial discharge due to electromagnetic interference [4]. However, due to the strong partial discharge, the influence of the parameters in the partial discharge model based on the acoustic emission detection technology is not quite clear, and it is also difficult to quantitatively analyze the discharge quantity [5]. Based on the counting method and energy analysis method in acoustic emission detection technology, as well as the time domain characteristics and frequency domain characteristics of acoustic emission signals generated by partial discharge, the physical factors influencing the partial discharge characteristics are studied in this paper.

2 Acoustic Emission Detection and Analysis of Partial Discharge 2.1

Experimental Design of Partial Discharge Model

Design of the needle-plate partial discharge system is shown in Fig. 1. The transformer is 50 kV test transformer. Discharge is 5 pC with 50 kV. Previous studies have shown that the spectrum of partial discharge acoustic emission signals was mainly concentrated in more than 100 kHz, so the SR150M sensor was selected. After the sensor signal is processed by the preamplifier, TDS2014B, and acoustic emission detector SAEU2S, it is passed to the computer using the LabVIEW and USBAE to obtain data analysis and processing. The structure of the needle-plate model is shown in Fig. 2, consisting of the needle electrode and plate electrode, which is brass material. To discuss the physical factors that affect the partial discharge characteristics of the needle plate model, different needle plate spacings, needle electrode tapers, and plate electrode diameters were designed. The needle electrode is set with four different tapers, 25
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and 55
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Detection and Analysis of Partial Discharge Signal Based on Acoustic Emission

The generation of partial discharges is a process of charge neutralization. With the effect of current pulses, the area where the partial discharge occurs is thermally expanded, resulting in an explosion-like effect, and the initial state of the discharge area after the discharge is discharged [6, 7]. The partial discharge caused by the partial area of the volume of the moment of expansion and contraction causes the density of the media changing in the formation of elastic waves, spreading from the partial discharge area to the surrounding area. The elastic wave signal can be converted into a weak electrical signal by an acoustic emission sensor, thereby achieving acoustic emission detection of the partial discharge signal.

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3 Test Results and Analysis The ladder step method was used, and the acoustic emission signals produced by partial discharge of the needle-plate were collected by acoustic emission sensors.

3.1

Counting and Energy Analysis

Figure 4 is the acoustic emission signal waveform obtained from a partial discharge experiment with unchanged experimental conditions. It can be seen that the acoustic emission signal of partial discharge under the same experimental conditions is relatively random, and the influence law of the needle-plate model parameter change on the partial discharge signal is difficult to directly find. Therefore, from a statistical point of view, counting method and energy analysis method are used to analyze the data of the needle-plate partial discharge model acoustic emission signal to find out the influence law of the needle-plate model parameter on the partial discharge acoustic emission signal. Fig. 4 Needle-plate model partial discharge acoustic emission signal

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changes. Due to the randomness of the partial discharge, the initial discharge voltage measurement error was large. Therefore, the test method was used to reduce the measurement error of the initial discharge voltage by multiple tests. For each needleplate spacing, six tests were repeated and the average value is obtained. It can be seen from the figure that, as the needle-plate spacing gradually increases, the initial discharge voltage gradually increases, which is consistent with the actual situation. The graphs in Fig. 7 were for count analysis and energy analysis during the partial discharge process with the spacing of 0, 1, and 2 mm between needle electrode and epoxy board. When the spacing of the needle plate was increased, the frequency and energy of the partial discharge were greatly reduced, and the development trend of the partial discharge was similar with the different spaces as voltage increases. As the needle plate spacing increases, leading to reduce the field between the needle plate, space electric field distortion was reduced, so the discharge frequency and discharge energy were reduced. The results of the plate electrode diameter of 70, 100, and 150 mm were shown in Fig. 8. It was found that when the diameter of the plate electrode increases, the number of discharges and the discharge energy would drop, which should be related to the current distribution in the electrode.

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Time-Frequency Analysis of Partial Discharge Acoustic Emission Signals

In order to further simulate the partial discharge under different conditions, the partial discharge experiments under different voltages were carried out. It was found that the change of the voltage and the parameters of the needle plate model have little effect on the partial discharge time-frequency characteristic. However, compared with the partial discharge acoustic emission signal with same voltage, it was found that the signal amplitude difference was large and the time-frequency characteristics were different. Figures 9 and 10 showed the time-frequency analysis graph of partial discharge acoustic emission waveforms with two different amplitudes during an experiment. The spectral content of the partially emitted acoustic emission signal changing with time was described. When the amplitude of the partially emitted acoustic emission signal was small, the signal energy was dispersed but mainly distributing in the range of 220–270 kHz. When the amplitude of the partial discharge acoustic emission signal was large, the signal energy gathers in the range of 130–170 kHz, and the other frequency sections almost have no energy distribution. Therefore, the partial discharge mode recognition based on STFT should consider two signals with different amplitudes which may be the partial discharge signal under the same models. Based on acoustic emission technology’s partial discharge test, it is necessary to use not only a sensor with a resonant frequency of 150 kHz typical values but also a wider band acoustic emission sensor. Fig. 9 Time-frequency analysis of acoustic emission signal with small amplitute

Fig. 10 Time-frequency analysis of acoustic emission signal with large amplitute

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4 Conclusion Due to the strong randomness of PD signals, the partial discharge signals are quite different even under the same experimental conditions. AE signals for single partial discharge signals are difficult to quantitatively analyze the whole PD system. By analyzing the partial discharge AE signal with counting and energy analysis, the disturbance of single partial discharge signal randomness to the quantitative analysis of the whole system is reduced. In addition, the energy distribution of PD signals at different amplitudes is different, and it could be used to distinguish the phenomena in partial discharge signal pattern recognition.

References 1. K. Qin, C. Pan, K. Wu, Effect of needle curvature and aluminum foil size on discharge mechanism under needle-plate configuration. High Volt. Eng. 38(7), 172–175 (2012) 2. H. Borsi, Study about the physical processes leading to partial discharges in insulating fluids. Eur. T. Electr. Power 10(3), 185 (2007) 3. L.B. He, R.W. Zhang, H.J. Zhu, P. Yang, Realization of piezoelectric acoustic emission sensor calibration by reciprocity method. Acta Metrol. Sin. 35(5), 479–483 (2014) 4. A. Klink, M. Holsten, S. Schneider, Acoustic emission signatures of electrical discharge machining. CIRP Ann. 65, 229 (2016) 5. T.F. Sipahutar, A.A. Kemma, N. Pattanadech, Effect of test method and needle plane configuration on partial discharge inception voltage measurement of mineral oil based on Weibull analysis. Proc. Tech. 11, 411–418 (2013) 6. C.S. Xu, Z.Q. Sun, X.F. Zhao, M.Y. Li, Experimental investigation of the partial discharge feature with the air gap model in oil-paper insulation. High Volt. Appar. 48(02), 34–41 (2012) 7. X.D. Xue, X.D. Cheng, B. Xu, Time-frequency analysis of partial discharge signals of highvoltage electrical devices by STFT. High Volt. Eng. 34(01), 70–72 (2008)

Application of Multistep Source Localization Method with Narrowing Velocity Interval in Mines Longjun Dong, Daoyuan Sun, Weiwei Shu, Xibing Li, and Jian Wang

Abstract The heterogeneity of the rockmass and the premeasured P-wave velocity significantly affect the accuracy of the source localization. Based on the arrival time difference algorithm, a multistep localization method with narrowing velocity interval was proposed, in which the premeasured P-wave velocity is not needed. The minimum and maximum velocity values of the former results were used as the initial velocity interval of the next localization. The preliminarily optimized source coordinates and the re-narrowed P-wave velocity interval were calculated by loop computation. The optimized source coordinates and the best velocity interval were selected until the velocity differences of the latest three velocity interval were less than the threshold. The proposed multistep localization method was applied to locate the microseismic sources of Kaiyang phosphorus mine. The accuracy of localization results was compared with the results of one-step localization. Results show that the multistep localization method is obviously superior to the traditional localization method. The multistep localization method highlights two outstanding advantages: it can eliminate the errors caused by the premeasured velocity; it can improve the locating accuracy in the heterogeneous media. Therefore, the multistep localization method is an accurate and effective method for the microseismic source localization in mining engineering. Keywords Microseismic monitoring · Multi-step localization · Wave velocity interval · Optimization

1 Introduction It has been a new normalcy that deep mining projects are carried out in many countries, including South Africa, Canada, Australia, America, and China, due to the increasing demand for valuable resources and the development of mining

L. Dong (*) · D. Sun · W. Shu · X. Li · J. Wang School of Resources and Safety Engineering, Central South University, Changsha, China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_37

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technology [1–3]. However, the deep rockmass will be disturbed by increased geostress, as well as the dynamic and continuous mining in multilevel and multistope, which may cause the instability of the rockmass [4, 5]. As a result, the dangerous disasters including rockmass deformation, roof collapse, tailings dam failure, and even rockburst will be induced, which may pose destructive hazards to the safety of workers and equipment [6–10]. Currently, the microseismic monitoring system is the most effective method for geostress monitoring and controlling [11– 13]. Based on the real-time monitoring and numerical simulation, Dong et al. established a pre-alarm model for tailing dam, in which the Internet of things and the cloud computing are considered [14]. Ma et al. applied the full waveform inversion and statistical methods to investigate the different microseismic source mechanisms in an underground mine [15–17]. It is feasible to achieve long-term monitoring and short-term forecasting for microseismicity in the complex underground structure, while its effectiveness depends on the accurate and efficient localization method, a fundamental and significant problem for the microseismic monitoring technology. In general, the current localization methods can be classified into two groups, where one is the iterative localization method and another is the analytical localization method. Through the established nonlinear governing equations for a microseismic event, the source coordinates can be solved according to the derived explicit analytical formulas, which is exactly the main thought of the analytical localization method [18, 19]. Traditionally, the P-wave velocity is taken as a known parameter in many analytical localization methods including INGLADA and USBM methods [20]. For instance, through the spherical interpolation method and the least square method, Chan and Ho developed the 3D closed-form solutions using arrival time difference [21]. Nevertheless, the above localization methods fail to consider the temporal and spatial errors caused by predetermined P-wave velocity. Focusing on this important issue, Dong et al. proposed the 3D analytical solutions without predetermined P-wave velocity successively for cube, cuboid, and random monitoring networks contained six sensors [22–24]. To solve the problem that more than six sensors are deployed in the practical engineering, the source coordinates can also be calculated through the combination of the analytical solutions and probability density functions [25]. However, it is common that the abnormal arrivals are recorded in the deep mining process, which directly affect the accuracy of input data and then the analytical localization accuracy [26]. Compared to the analytical localization methods, the iterative localization methods are more applicable to the deep mining environment with dynamic disturbances, since the optimal localization results can be investigated through multiple sensors and numerous iterations. Based on the thought of Geiger, numerous optimized iterative localization methods were proposed to locate the earthquake hypocenters [27–30]. However, these methods are developed in the field of geophysics, where the wave velocity is taken as a premeasured parameter using large numbers of hypocenters and arrivals. In the dynamic mining environment, the stress adjustment and the change of rockmass structure will change the regional P-wave velocity, which results in that the velocity model using premeasured velocity is not suitable

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and applicable. To eliminate the location error caused by premeasured P-wave velocity in complex and dynamic mining environment, Dong et al. [31, 32] presented an iterative localization method to locate the microseismic events and the blasting experiments, which takes the time difference (TD) as the dependent variable. Although the TD method is relatively accurate and efficient for sources localization, the velocity interval is not constrained, which means that the velocity value used in the computational process is only greater than 0. Thus, there will be a long computation time due to the great range of P-wave velocity. In addition, it is possible to obtain a local optimum, instead of the global optimum, using TD method. It is important to develop an accurate localization method for solving the global optimum with high computational efficiency, which can be applied to the complex and dynamic mining environment. In our previous studies, through the narrowing and optimization of the velocity interval, a multistep localization method without premeasured velocity is proposed, to improve the location accuracy and computational efficiency in buildings [33]. The multistep localization method is expected to provide useful information for the accurate localization of invisible rock cracks and the determination of potential hazard areas. In this paper, the multistep localization method is applied to locating the microseismic sources of Kaiyang phosphorus mine.

2 Overview of the Multistep Localization Method The calculation process of the multistep localization method is shown in Fig. 1. All of the data needed were obtained by the microseismic system, supposing that the coordinates of a microseismic source are P(x, y, z) and the coordinates of the triggered sensors are Si ¼ (xi, yi, zi). The average P-wave velocity is v. The first step localization for the microseismic sources was carried out using the TD algorithm with the initial velocity interval of [0, 10000] m/s. Among the locating l l results, the maximum velocity vmax and the minimum velocity vmin were  obtained.  l l ; vmax The calculated source coordinates and the narrowed velocity interval vmin were used as the initial values for the second step
localization. Similarly, the i i i preliminarily optimized source coordinates x ; y ; z 0 0 0 and the re-narrowed P-wave  l  l were calculated by loop computation. The optimized ; vmax velocity interval vmin source coordinates and the best velocity interval were selected until the latest velocity interval and the two narrowed velocity interval before satisfied the conditions of Eq. (1). i v  vi1 < n, v i  vi1 < n max max min min i1 v  vi2 < n, vi1  vi2 < n max max min min

ð1Þ

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Fig. 1 The flowchart for locating the microseismic sources using the multistep localization method i i1 i2 i2 where v1min , vmax , vi1 min , vmax , vmin , and vmax indicate the minimum and the maximum velocity of the latest three calculated results, respectively. n is the threshold of the velocity difference. Considering that the traditional localization method is still widely used, the effectiveness and the accuracy of the multistep localization method are compared with the results of the traditional localization method. As a result, the number of the effective localization results is more than the number of the traditional localization method, which indicates that the locating accuracy with the narrowed and optimized velocity interval is improved.

3 Results and Discussions The surface elevation and current mining depth of Kaiyang phosphorous mine are about +1500 m and more than 700 m, respectively. The relative mining depth is approximately 800 m, which belongs to the deep mining. In the early mining stage,

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the open stope mining method is large-scale used, which resulted in numerous underground goafs in the underground mining area and the phenomenon of local stress concentration [34]. The large-area rockmass instability or the rockburst may be induced at a time. Thus, a 32-channel microseismic monitoring system was established in the Yongshaba mine, the main mining area of Kaiyang phosphorous mine, to avoid the destructive disasters. A total of 26 single-component sensors and two 3-component sensors are distributed on the transport tunnels in 930, 1080, and 1120 levels. The natural frequency of the used sensors is 14  1 Hz, and the response frequency is from 50 Hz to 5 KHz [26]. The location of Kaiyang phosphorous mine and the layout of the microseismic sensors are shown in Fig. 2. The coordinates of each sensor are listed in Table 1.

Fig. 2 (a) The location of Kaiyang phosphorous mine; (b) map view of the monitoring area: the blue triangles show the microseismic sensors, and the red points show the events in this area

Table 1 The coordinates of each sensor of the microseismic monitoring system Sensor ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14

x/m 2,995,768.32 2,996,238.50 2,996,639.11 2,996,780.77 2,996,922.06 2,997,074.43 2,997,259.34 2,997,379.52 2,997,590.28 2,997,783.62 2,997,812.94 2,997,655.59 2,997,481.75 2,997,311.53

y/m 380,976.56 381,095.98 381,293.46 381,419.76 381,454.80 381,387.86 381,320.15 381,306.67 381,279.15 381,263.74 381,594.24 381,591.01 381,676.23 381,602.69

z/m 946.52 952.94 950.01 946.06 943.68 950.66 946.63 951.39 951.88 944.27 1083.76 1084.26 1080.45 1081.49

Sensor ID 15 16 17 18 19 20 21 22 23 24 25 26 27 28

x/m 2,997,093.09 2,996,951.03 2,996,793.58 2,996,623.81 2,996,443.46 2,996,284.23 2,996,092.02 2,995,857.80 2,998,138.86 2,997,989.02 2,997,850.48 2,997,691.70 2,996,013.86 2,996,447.16

y/m 381,653.87 381,554.87 381,459.11 381,385.38 381,353.89 381,416.19 381,287.14 381,256.53 381,735.92 381,697.15 381,677.02 381,692.57 381,052.56 381,258.30

z/m 1076.37 1083.12 1078.15 1083.40 1083.13 1083.29 1084.81 1085.14 1136.97 1138.04 1135.28 1132.39 953.88 943.29

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Fig. 3 The locating results of the traditional localization method, with the color scale that shows the altitude of the events

From January to April 2014, 1891 microseismic events were recorded by the IMS microseismic monitoring system of Kaiyang phosphorous mine. Two hundred of the 1891 microseismic events are randomly selected to verify the accuracy and effectiveness by locating the source coordinates. A microseismic event whose coordinates of x and y are out of the monitoring area is defined as an ineffective one. The traditional localization method with the premeasured wave velocity of 4500 m/s was first used. For the 200 microseismic events, the number of effective events is 173. The locating results are shown in Fig. 3, with a color scale indicates the altitude of the events. From Fig. 3 we can clearly find that the locating results are of big discreteness. Some of the points are located in the edge of the monitoring area, where no mining work is conducted. The multistep localization method is used to locate the source coordinates. Considering the heterogeneity of the rockmass, the initial velocity interval and the threshold n are set as [0, 10000] m/s and 100 m/s. The number of the effective events is 178. The results of the localization are shown in Fig. 4a. Figure 4a clearly indicates that most of the effective events are located in the area of the tunnels, where a great deal of mining work is being conducted. The minimum and the maximum values of the calculated wave velocity in the first step localization are 1946.88 and 9447.76 m/s. Therefore, the velocity interval of the second localization is set as [1946, 9447]m/s. One Hundred seventy-seven out of 200 events are located in the mining area. The lower limit and the upper limit of the calculated wave velocity are 3107.69 and 9065.38 m/s. According to the results, the third step localization was conducted. The number of the effective events is 179. The minimum and the maximum values of the wave velocity are 3114.06 and 9009.58 m/s.

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Fig. 4 The locating results of the multistep localization method: (a) the velocity interval is [0, 10000]m/s; (b) the velocity interval is [3114, 9009]m/s

Here, the velocity difference of the lower limit and the upper limit is both less than the threshold 100 m/s. It can be considered that the wave velocity is tending toward stability. Thus, the last velocity interval is set as [3114, 9009]m/s. The locating results are shown in Fig. 4b. There also have 179 effective events located in the mining area. For the 200 microseismic events, the numbers of effective events for the traditional localization method and the multistep localization method are 173 and

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179, which indicate that the multistep localization method is more effective than the traditional localization method. From Figs. 3 and 4, we can find that almost all the locating results of the multistep localization method are located in the mining area, while some results of the traditional localization method are located in the edge of the mining area, which indicates that the accuracy of the multistep localization method is higher than the result of the traditional localization method.

4 Conclusions The frequent microseismic events significantly affect the stability of the mining stope; thus the accurate source localization is of critical importance. However, the heterogeneity of the rockmass and the premeasured P-wave velocity seriously affect the accuracy of the source localization. To eliminate the errors caused by the premeasured velocity and improve the locating accuracy, a multistep localization method was proposed in our previous study. In this paper, the multistep localization method was applied to locating the microseismic sources of Kaiyang phosphorous mine. The localization results of 200 randomly selected microseismic events were compared with the results of the traditional localization method. Results show that the effective events located by the two methods are 179 and 173, which indicates that the multistep localization method is more effective than the traditional localization method. The comparison also indicates that the results of the traditional localization method are more discrete than the results of the multistep localization method. Almost all the located events of the multistep localization method concentrated on the area of the mining tunnels. For the traditional localization method, many of the located events located in the edge of the mining area, where no mining work is conducted. According to the practical mining project, the microseismic events are mainly induced by the mining work. Hence we can conclude that the multistep localization method is more accurate than the traditional localization method. Acknowledgment The authors wish to acknowledge financial support from the National Natural Science Foundation of China (51504288), National Basic Research Program of China (2015CB060200), China Postdoctoral Science Foundation (2015M570688, 2016T90639), The Young Elite Scientists Sponsorship Program by CAST (YESS20160175), and Innovation-Driven Project of Central South University (2016CXS001).

References 1. L.J. Dong, J. Wesseloo, Y. Potvin, X.B. Li, Discriminant models of blasts and seismic events in mine seismology. Int. J. Rock Mech. Min. Sci. 86, 282–291 (2016) 2. L.J. Dong, J. Wesseloo, Y. Potvin, X.B. Li, Discrimination of mine seismic events and blasts using the Fisher classifier, naive Bayesian classifier and logistic regression. Rock Mech. Rock Eng. 49(1), 183–211 (2016)

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3. L.J. Dong, X.J. Tong, X.B. Li, J. Zhou, S.F. Wang, B. Liu, Some developments and new insights of environmental problems and deep mining strategy for cleaner production in mines. J. Clean. Prod. 210, 562–1578 (2019) 4. L.J. Dong, D.Y. Sun, X.B. Li, Z.L. Zhou, Interval non-probabilistic reliability of a surrounding jointed rockmass in underground engineering: a case study. IEEE Access 5, 18804–18817 (2017) 5. L.J. Dong, D.Y. Sun, X.B. Li, J. Ma, L.Y. Zhang, X.J. Tong, Interval non-probabilistic reliability of surrounding jointed rockmass considering microseismic loads in mining tunnels. Tunn. Undergr. Sp. Tech. 81, 326–335 (2018) 6. G.L. Feng, X.T. Feng, B.R. Chen, Y.X. Xiao, Y. Yu, A microseismic method for dynamic warning of rockburst development processes in tunnels. Rock Mech. Rock Eng. 48(5), 2061– 2076 (2015) 7. L.J. Dong, W.W. Shu, X.B. Li, Z.L. Zhou, F.Q. Gong, X.L. Liu, Quantitative evaluation and case study of risk degree for underground goafs with multiple indexes considering uncertain factors in mines. Geofluids 2017, 1–15 (2017). Article ID 3271246 8. H. He, L.M. Dou, A.Y. Cao, J. Fan, Mechanisms of mining seismicity under large scale exploitation with multikey strata. Shock Vibrat. 2015, 1–9 (2015). Article ID 313069 9. L.J. Dong, D.Y. Sun, X.B. Li, Theoretical and case studies of interval nonprobabilistic reliability for tailing dam stability. Geofluids 2017, 1–11 (2017). Article ID 8745894 10. X.T. Feng, S. Webber, M.U. Ozbay, Neural network modeling on assessing rockburst risks for South African deep gold mines. Trans. Nonferrous Met. Soc. Chin. 8(2), 1–7 (1998) 11. C.P. Lu, G.J. Liu, N. Zhang, T.B. Zhao, Y. Liu, Inversion of stress field evolution consisting of static and dynamic stresses by microseismic velocity tomography. Int. J. Rock Mech. Min. Sci. 87, 8–22 (2016) 12. P.H. Zhang, T.H. Yang, Q.L. Yu, T. Xu, W.H. Shi, Study of a seepage channel formation using the combination of microseismic monitoring technique and numerical method in Zhangmatun iron mine. Rock Mech. Rock Eng. 49, 3699–3708 (2016) 13. Y. Potvin, M.R. Hudyma, Seismic monitoring in highly mechanized hardrock mines in Canada and Australia, The Fifth International Symposium on Rockburst and Seismicity in Mines, 2011, pp. 267–280. 14. L.J. Dong, W.W. Shu, D.Y. Sun, X.B. Li, L.Y. Zhang, Pre-alarm system based on real-time monitoring and numerical simulation using internet of things and cloud computing for tailings dam in mines. IEEE Access 5, 21080–21089 (2017) 15. J. Ma, L.J. Dong, G.Y. Zhao, X.B. Li, Discrimination of seismic sources in an underground mine using full waveform inversion. Int. J. Rock Mech. Min. Sci. 106, 213–222 (2018) 16. J. Ma, L.J. Dong, G.Y. Zhao, X.B. Li, Focal mechanism of mining-induced seismicity in fault zones: a case study of yongshaba mine in China. Rock Mech. Rock Eng. (2019). https://doi.org/ 10.1007/s00603-019-01761-4 17. J. Ma, L.J. Dong, G.Y. Zhao, X.B. Li, Ground motions induced by mining seismic events with different focal mechanisms. Int. J. Rock Mech. Min. Sci. 116, 99–110 (2019) 18. L.J. Dong, X.B. Li, G.N. Xie, An analytical solution for acoustic emission source location for known P wave velocity system. Math. Probl. Eng. 2014, 1–6 (2014). Article ID 290686 19. R. Duraiswami, D. Zotkin, L. Davis, Exact solutions for the problem of source location from measured time differences of arrival. J. Acoust. Soc. Amer. 106(4), 2277 (1999) 20. M.C. Ge, Analysis of source location algorithms: Part I: Overview and non-iterative methods. J. Acoust. Emission 21(1), 29–51 (2003) 21. Y.T. Chan, K.C. Ho, An efficient closed-form localization solution from time difference of arrival measurements, IEEE Int. Conf. Acoust., Speech, Signal Process., Adelaide, SA, Australia, 1994, pp. II-393–II-396. 22. L.J. Dong, X.B. Li, Three-dimensional analytical solution of acoustic emission or microseismic source location under cube monitoring network. Trans. Nonferrous Met. Soc. Chin. 22(12), 3087–3094 (2012)

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23. L.J. Dong, X.B. Li, Z.L. Zhou, G.H. Chen, J. Ma, Three-dimensional analytical solution of acoustic emission source location for cuboid monitoring network without pre-measured wave velocity. Trans. Nonferrous Met. Soc. Chin. 25(1), 293–302 (2015) 24. X.B. Li, L.J. Dong, An efficient closed-form solution for acoustic emission source location in three-dimensional structures. AIP Adv. 4(2), 1–8 (2014) 25. L.J. Dong, W.W. Shu, X.B. Li, G.J. Han, W. Zou, Three dimensional comprehensive analytical solutions for locating sources of sensor networks in unknown velocity mining system. IEEE Access 5, 11337–11351 (2017) 26. L.J. Dong, W. Zou, X.B. Li, W.W. Shu, Z.W. Wang, Collaborative localization method using analytical and iterative solutions for microseismic/acoustic emission sources in the rockmass structure for underground mining, Eng. Fract. Mech., https://doi.org/10.1016/j.engfracmech. 2018.01.032 27. K. Aki, W.H. Lee, Determination of three-dimensional velocity anomalies under a seismic array using first P arrival times from local earthquakes: Part 1: A homogeneous initial model. J. Geophys. Res. 81(23), 4381–4399 (1976) 28. G.L. Pavlis, J.R. Booker, The mixed discrete? continuous inverse problem: Application to the simultaneous determination of earthquake hypocenters and velocity structure. J. Geophys. Res.Solid Earth 85(B9), 4801–4810 (1980) 29. J. Pujol, Comments on the joint determination of hypocenters and station corrections. Bull. Seismol. Soc. Am. 78(3), 1179–1189 (1988) 30. G.D. Nelson, J.E. Vidale, Earthquake locations by 3-D finite difference travel times. Bull. Seismol. Soc. Am. 80(2), 395–410 (1990) 31. L.J. Dong, X.B. Li, L.Z. Tang, F.Q. Gong, Mathematical functions and parameters for microseismic source location without pre-measuring speed. Chin. J. Rock Mech. Eng. 30(10), 2057– 2067 (2011) 32. L.J. Dong, D.Y. Sun, X.B. Li, K. Du, Theoretical and experimental studies of localization methodology for AE and microseismic sources without pre-measured wave velocity in mines. IEEE Access 5, 16818–16828 (2017) 33. L.J. Dong, W.W. Shu, G.J. Han, X.B. Li, J. Wang, A multi-step source localization method with narrowing velocity interval of cyber-physical systems in buildings. IEEE Access 5, 20207– 20219 (2017) 34. L.J. Dong, W.W. Shu, X.B. Li, J.M. Zhang, Quantitative evaluation and case studies of cleaner mining with multiple indexes considering uncertainty factors for phosphorus mines. J. Clean. Prod. 183, 319–334 (2018)

Statistical Precursor of Induced Seismicity Using Temporal and Spatial Characteristics of Seismic Sequence in Mines Longjun Dong, Daoyuan Sun, Weiwei Shu, Xibing Li, and Lingyun Zhang

Abstract Induced seismicity associated with mining is becoming an increasingly important issue worldwide, and it poses a hazard to the exposed population and structures. In this work, the seismic sequence is analyzed with the aim of detecting changes in statistical parameters describing the seismic event occurrence before the main shocks, to be used for monitoring the sequence evolution. Temporal and spatial characteristics of seismic sequence before the large magnitude seismic events (MW > 1) in mines are used to investigate the precursor of large magnitude. The statistical results of 25 from 27 large magnitude seismic events implicate the following characteristics of precursor seismicity before the occurrence of the large magnitude seismic event of the sequence: Cumulative Benioff strain increases continuously. The Hurst exponent for the foreshocks is greater than 0.5. There is initially an increase in b value then a decrease. Three cases related to the statistical precursor are presented and discussed in the paper. The observed temporal and spatial variations of three selected indexes including b value, Hurst exponent, and cumulative Benioff strain support the hypothesis that three indexes have significant potential of statistical precursor. Keywords Statistical precursor · Seismic sequence · b value · Hurst exponent · Benioff strain

1 Introduction With the mining depth increasing year by year, dangerous disasters like rockbursts and a wide range of collapses induced by high stress not only threaten the safety production of mines seriously but also cause huge economic losses and casualties to mines in many countries with the characteristics of large magnitude, strong destructiveness, and wide areas [1–8]. L. Dong (*) · D. Sun · W. Shu · X. Li · L. Zhang School of Resources and Safety Engineering, Central South University, Changsha, China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_38

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As one of the most effective means in monitoring and early warning of underground disasters, the microseismic monitoring has been applied in numerous mines in Canada, Australia, South Africa, Poland, and China [9, 10]. By using an automatic multichannel monitoring system to detect the arrival times of seismic waves in a hard rock nickel mine in Sudbury, Canada, Young et al. [11] proved the feasibility of using microseismic monitoring system for mechanism studies and speculated that the shear failure is the predominant mechanism of failure at the source for low-magnitude mining-induced microseismic events. In 1989, microseismic monitoring systems were installed in several Canadian mines to capture complete waveforms and investigate seismic source parameters including first motion, peak particle velocity, seismic energy, and spectral frequency [12]. Through the application of the pure seismological inversions, the point source moment tensor is obtained to discriminate the type of the induced seismicity in a mine [13–15]. To reach a more complete understanding of rock damage at depth and mechanisms of instability, the ground deformation and microseismic data of Century Mine were analyzed [16]. To understand the coseismic and aseismic deformation of the rocks, the rate of tilt and the seismic ground motion were analyzed, and a good correspondence between the coseismic and the aseismic deformations was found [17]. The level on safety of mining operations was improved greatly due to the real-time source location of seismic events [18–22]. Morrison [23] researched methodologies to minimize the rockburst hazard by developing a simple model which could explain some of the rockburst occurrences. Linzer [24] proposed an appropriate method to calculate the focal mechanisms of seismic events induced by mining activities. Dong et al. developed the limit state equations of rock blocks under microseismic loads and proposed a method of the interval non-probabilistic reliability to analyze the stability of surrounding jointed rock mass [25]. Pastén et al. [26] used ROC analysis to research seismic hazard in mines and suggested that Dq decreases with time opens the possibility to generate a warning before the occurrence of a relevant event in a mine. Beck and Brady [27] presented a cell evaluation method for quantifying seismic hazard using modeling and analysis of records of mine seismic events. Considering the Internet of things and the cloud computing, Dong et al. established a pre-alarm model for tailing dam based on the real-time monitoring and numerical simulation [28]. In China, the shallow mineral resources are drying up after decades of continuous large-scale exploitation, and the mining depth is increasing gradually [29]. As of 2015, there are already more than 80 mines with a depth of kilometerscale, and the rate of deepening is about 10–25 m/a. However, the basic research of deep mining is weak and the induced rockbursts will cause huge casualties and waste abundant resources. Therefore, a fundamental breakthrough is urgently needed to solve disasters like rockbursts in the deep mining. Once the hazard of induced seismicity in mining areas is evaluated, it is feasible to achieve an early warning to protect the safety of workers according to the characteristics of seismic activities. It is interesting to note that most of the existing research methods always evaluate and warn for regional hazard based on the magnitude or the event rate which has happened [11, 12, 23, 24, 26, 27, 30]. In order to analyze the precursor characteristics of the large magnitude events, according to the temporal and spatial

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distribution characteristics of seismic activity parameters, the precursors of oncoming large magnitude events in local areas at a certain time were investigated comprehensively in this paper. It is hopeful to provide a reasonable and practical solution for the hazard evaluation in deep mining.

2 Methods 2.1

Gutenberg-Richter Magnitude-Frequency Relationship

In the study of seismicity, the complete and reliable seismic catalogue is very important for obtaining more accurate statistical results. The integrity of the seismic catalogue is directly affected by the minimum magnitude of completeness Mmin. Mmin refers to the minimum magnitude which can be detected in all seismic events in a specific temporal and spatial dimension. The research of seismicity has a practical significance only when the minimum magnitude of completeness in a particular area Mmin is determined. At present, there are two methods to determine the minimum magnitude of completeness Mmin: One is the seismic waveform method, and another is the statistical method [31]. The former one gets Mmin by analyzing the seismic waveform, but it isn’t adopted usually because of the need of dealing with numerous seismic data. The latter one is selected to calculate the Mmin since it takes the advantages of the Gutenberg-Richter magnitude-frequency relationship [32] to getting the best part of the linear relationship, and Mmin is exactly the minimum completeness magnitude of this part. The Gutenberg-Richter magnitude-frequency relationship is used in this paper to calculate the minimum magnitude of completeness Mmin, a, and b in an area: logN ¼ a  bM W

ð1Þ

where MW is the moment magnitude. N is the cumulative number of seismic event in the magnitude range (M  ΔM). a and b are empirical constants. The b value is an important parameter to measure the regional seismicity level and to describe the proportional relationship for numbers of large magnitude events and small magnitude events in the area. The calculation method for Mmin is introduced as follows: First, all the data were fitted linearly to get the line 1. There were about two intersections defined as m1 and n1 between the line 1 and the authentic data curve, which divided the authentic data curve into three parts roughly: (1) the part less than m1, (2) the part between m1 and n1, and (3) the part greater than n1. The point Mmin that we want to find is around m1, exactly between m1 and n1. It can be expressed as m1 < Mmin < n1. Second, the part less than m1 were dismissed. Then we took the remaining parts into consideration, another straight line called line 2 can be fitted. Similarly, there were two intersections m2 and n2 between the line 2 and the data curve. It can be easily found that the point

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m2 was closer to Mmin compared with the point m1. Third, repeating the second step until the point mi was approximately equal to Mmin.

2.2

Hurst Exponent

The Hurst exponent can be calculated by rescaled range analysis (R/S analysis) [33, 34]. For a time series of the moment magnitude for microseismic events, M ¼ M1, M2, . . ., Mn, R/S analysis method is described as follows: 1. Calculate the average value MA MA ¼

t 1X Mi t i¼1

ð2Þ

2. Calculate the average adjusted series Yi Y i ¼ Mi  MA,

i ¼ 1,2, . . . , t

ð3Þ

3. Calculate the cumulative deviate series Zi Zi ¼

t X

Y i,

i ¼ 1,2, . . . , t

ð4Þ

i¼1

4. Calculate the range series Rm Rm ¼ maxðZ 1 ; Z 2 ; . . . ; Z t Þ  minðZ 1 ; Z 2 ; . . . ; Z t Þ

ð5Þ

5. Calculate the standard deviation series Sm vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u t u1 X Sm ¼ t ðM i  M A Þ2 , t i¼1

i ¼ 1,2, . . . , t

ð6Þ

6. Calculate the rescaled range series (R/S) ðR=SÞ ¼ Rm =Sm ,

m ¼ 1,2, . . . , M

ð7Þ

Note that (R/S) is averaged over the regions [M1, Mt], [Mt+1, M2t] until [M(M1)t+1, MMt] where m ¼ floor (n/t). In practice, to use all data for calculation, a value of t is chosen which is divisible by n.

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Hurst found that (R/S) scales by power law as time increases, which indicates that ðR=SÞt ¼ c t H

ð8Þ

Here c is a constant and H is called the Hurst exponent. To estimate the Hurst exponent, we plot (R/S) versus t in log-log axes. The slope of the regression line approximates the Hurst exponent. For t < 4, (R/S)t is not accurate; thus we shall use a region of at least four values to calculate the rescaled range. There is not a standard value for H. Different physical objects correspond to different H exponents. In general, H exponent is between 0.5 and 1 for some physical phenomena. It is widely accepted that there is a kind of persistence when H > 0.5 which means the latter events are correlated with the former events positively. Similarly, the latter events correlating with the former events negatively are called antipersistence when H < 0.5. As for seismic events, they have long range correlation and the latter events would be affected by the former events. It is regular and random for the occurrence of seismic events, the degree that H exponent deviates from 0.5 can measure the proportion of random factors and certain factors in the seismic time distribution. In the range of H > 0.5, the decrease of H exponent means that the proportion of random factors in the distribution of seismic time is increasing. It is worthy to explore the possible dangerous time for the large magnitude microseismic event using H exponent.

2.3

Benioff Strain

Some seismologists [35, 36] have observed the seismic activation prior of a lot of major earthquakes and quantified as accelerated releases of the cumulative (square root sum) of seismic energy in the time series. The power exponent m in the above formula is approximately equal to 0.3 when using the Benioff strain [37]. The calculating method of Benioff strain is shown as Bi ¼

X pffiffiffiffiffi X pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 101:695M i þ10:18 Ei ¼

ð9Þ

where Mi is the moment magnitude of seismic event i. Bufe and Varnes [35] concluded that the cumulative Benioff strain is more accurate than the cumulative seismic moment in the aspect of forecasting large magnitude seismic events.

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3 Results and Discussions To analyze the precursor behaviors of large magnitude events in mines, the following parameters are considered: the minimum magnitude of completeness Mmin, the maximum magnitude Mmax (The maximum magnitude in the specific temporal and spatial area. In this paper, it refers to the maximum magnitude in the time window.), the standard deviation St, the b value, the ratio of a value to b value, the Hurst exponent, Benioff strain, and cumulative Benioff strain. Firstly, we determined the early warning area of large seismic events and took the center point of this area as the spherical point. The radius of the sphere was set to R which is influenced by mine production, the number of sensors in the monitoring network, location error, and level of sensitivity. In this work, we analyzed the situations of R ¼ 50 m, R ¼ 100 m, R ¼ 150 m, R ¼ 200 m, R ¼ 250 m, R ¼ 300 m, R ¼ 350 m, and R ¼ 400 m, respectively, and found that accurate locating and high sensitivity could narrow the early warning area. Of course, the time length of the sample window also affects the early warning area. We mainly used the time to control the length of the sample window. By analyzing and comparing, the time length of each window was fixed as 7 days (the time window can be adjusted according to practical mining environment and event distribution). The widow is named by the minimum date of the events. Then we can determine R by the following rules: If there are ten or more than ten events in each window when R ¼ 50 m, we could accept it. Otherwise, we should increase the radius R progressively by 50 m until the events of each window are not less than 10. The Mmin, Mmax, St, b, the ratio of a value to b value, H, Bi, and cumulative Benioff strain for each window were calculated. Here we define the large seismic event with a moment magnitude greater than 1. The temporal and spatial characteristics of the seismic sequence before the large magnitude events in four mines are used to investigate the precursory patterns prior to larger events. Three typical regions are used to explain the precursory features. The radius of focused space is taken as 100 m. The changes of microseismic activity parameters in the space and time window are analyzed every 7 days. The parameters for precursory features of the large magnitude events are analyzed, which provide the basis for the early warning of large magnitude events. For the first magnitude large event, it is very easy to find that it has the following characteristics from Fig. 1 Cumulative Benioff strain shows a trend of slow and sustained increase. The H exponent is greater than 0.5, which exhibits the characteristics of first decrease and then increase. The change of b value follows the “increase-decrease” characteristic. For the second and third large magnitude events in Figs. 2 and 3, the cumulative Benioff strain shows the characteristic of moderate and continuous increase. The b value exhibits the characteristic of “increase-decrease.” The H exponent shows the characteristic of first decrease and then increase. The statistical results of 25 from 27 seismic large magnitude events indicate the following characteristics of precursory patterns:

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(a) 2.0 3600 1.5

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1.2 1.5

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0.0

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1.0

2011/6/13 b value

0.7

2011/6/27

2011/7/11

Time

M

2011/7/25

H

Fig. 1 Temporal and spatial variations of three indexes before the occurrence of the large magnitude seismic event (Mw ¼ 1.6) of the sequence: (a) cumulative Benioff strain, (b) b value and H. The yellow area is tagged as windows for the precursory features with 3 weeks

1. The H exponent within the vicinity of a forthcoming large event is greater than 0.5, which means that the latter events can be related with the former events. 2. The b value takes the trend of “increase-decrease.” 3. The cumulative Benioff strain increases continuously.

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(a) 1.5

7000

1.0

6000

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4000 0.0 3000 -0.5

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0

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5000

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0.7 -1.0

0.80 2010/8/30 b value

2010/9/13

Time

M

2010/9/27

2010/10/11

0.6

H

Fig. 2 Temporal and spatial variations of three indexes before the occurrence of the large magnitude seismic event (Mw ¼ 1.1) of the sequence: (a) cumulative Benioff strain, (b) b value and H exponent. The yellow area is tagged as windows for the precursory features with 4 weeks

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(a) 1.1

18000

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0.8

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2010/5/10 M

2010/5/24

H

Fig. 3 Temporal and spatial variations of three indexes before the occurrence of the large magnitude seismic event (Mw ¼ 1.01) of the sequence: (a) cumulative Benioff strain, (b) b value and H exponent. The yellow area is tagged as windows for the precursory features with 4 weeks

4. There was no significant characteristic of the Mmin, Mmax, St, the ratio of a value to b value, and Bi. We also observed that 15 of 25 large magnitude microseismic events follow the precursory characteristic: The H exponent takes the trend of “decrease-increase” within 3 or 4 weeks.

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4 Conclusions The microseismic activity parameters of the large magnitude events including b value, H exponent, and cumulative Benioff strain are analyzed statistically. Through the analysis of 27 large magnitude events, we noticed that the microseismic activity parameters before 25 large magnitude seismic events have the following significant precursory characteristics: 1. The H exponent within the vicinity of a forthcoming large event is greater than 0.5, which means that they have correlation and the latter events would be affected by the former events. 2. The time length of the precursory characteristic is about 3 or 4 weeks. 3. The b value takes the trend of “increase-decrease” in windows with 3 or 4 weeks for the precursory features. 4. The cumulative Benioff strain increases continuously. It is noted that 15 of 25 large magnitude seismic events have the precursory characteristic: The H exponent takes the trend of “decrease-increase” within 3 or 4 weeks before large magnitude seismic events. The above precursory phenomena have a directive significance for early warning of large magnitude seismic events. Three cases related to the statistical precursor are presented and discussed in the paper. Acknowledgment The authors wish to acknowledge financial support from the National Natural Science Foundation of China (51504288), National Basic Research Program of China (2015CB060200), China Postdoctoral Science Foundation (2015M570688, 2016T90639), The Young Elite Scientists Sponsorship Program by CAST (YESS20160175), and Innovation-Driven Project of Central South University (2016CXS001).

References 1. D.A.J. Mendecki, Seismic Monitoring in Mines (Chapman & Hall, London, 1997) 2. V.A. Mansurov, Prediction of rockbursts by analysis of induced seismicity data. Int. J. Rock Mech. Min. Sci. 38, 893–901 (2001) 3. G.L. Feng, X.T. Feng, B.R. Chen, Y.X. Xiao, Microseismic sequences associated with rockbursts in the tunnels of the Jinping II hydropower station. Int. J. Rock Mech. Min. Sci. 80, 89–100 (2015) 4. L. Driad-Lebeaua, F. Lahaiea, M. Al Heiba, J.P. Josienb, P. Bigarréa, J.F. Noirelc, Seismic and geotechnical investigations following a rockburst in a complex French mining district. Int. J. Coal Geol. 64, 66–78 (2005) 5. W.D. Ortlepp, T.R. Stacey, Rockburst mechanisms in tunnels and shafts. Tunn. Undergr. Sp. Tech. 9(1), 59–65 (1994) 6. L.J. dong, D.Y. Sun, X.B. Li, Z.L. Zhou, Interval non-probabilistic reliability of a surrounding jointed rockmass in underground engineering: a case study. IEEE Access 5, 18804–18817 (2017)

Statistical Precursor of Induced Seismicity Using Temporal and Spatial. . .

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7. L.J. Dong, W.W. Shu, X.B. Li, J.M. Zhang, Quantitative evaluation and case studies of cleaner mining with multiple indexes considering uncertainty factors for phosphorus mines. J. Clean. Prod. 183, 319–334 (2018) 8. L.J. Dong, X.J. Tong, X.B. Li, J. Zhou, S.F. Wang, B. Liu, Some developments and new insights of environmental problems and deep mining strategy for cleaner production in mines. J. Clean. Prod. 210, 562–1578 (2019) 9. L.J. Dong, J. Wesseloo, Y. Potvin, X.B. Li, Discriminant models of blasts and seismic events in mine seismology. Int. J. Rock Mech. Min. Sci. 86, 282–291 (2016) 10. L.J. Dong, J. Wesseloo, Y. Potvin, X.B. Li, Discrimination of mine seismic events and blasts using the Fisher classifier, naive Bayesian classifier and logistic regression. Rock Mech. Rock Eng. 49(1), 183–211 (2016) 11. R.P. Young, S. Talebi, D.A. Hutchins, T.I. Urbancic, Analysis of mining-induced microseismic events at Strathcona Mine, Sudbury, Canada, in Seismicity in Mines, (Birkhäuser, Basel, 1989), pp. 455–474 12. D.G.F. Hedley, J.E. Udd, The Canada-Ontario-industry rockburst project, in Seismicity in Mines, (Birkhäuser, Basel, 1989), pp. 661–672 13. J. Ma, L.J. Dong, G.Y. Zhao, X.B. Li, Discrimination of seismic sources in an underground mine using full waveform inversion. Int. J. Rock Mech. Min. Sci. 106, 213–222 (2018) 14. J. Ma, L.J. Dong, G.Y. Zhao, X.B. Li, Focal mechanism of mining-induced seismicity in fault zones: a case study of yongshaba mine in China. Rock Mech. Rock Eng. (2019). https://doi.org/ 10.1007/s00603-019-01761-4 15. J. Ma, L.J. Dong, G.Y. Zhao, X.B. Li, Ground motions induced by mining seismic events with different focal mechanisms. Int. J. Rock Mech. Min. Sci. 116, 99–110 (2019) 16. M. Salvoni, P.M. Dight, Rock damage assessment in a large unstable slope from microseismic monitoring - MMG Century mine (Queensland, Australia) case study. Eng. Geol. 210, 45–56 (2016) 17. A. Milev, et al., Coseismic and aseismic deformations associated with mining-induced seismic events located in deep level mines in south Africa, in 13th SAGA Biennial Conference & Exhibition, Session 10 C – Seismology, Oct. 2013 18. L.J. Dong, X.B. Li, A microseismic/acoustic emission source location method using arrival times of PS waves for unknown velocity system. Int. J. Distrib. Sens. Netw. 2013, 1–8 (2013). https://doi.org/10.1155/2013/307489. Article ID 307489 19. X.B. Li, L.J. Dong, An efficient closed-form solution for acoustic emission source location in three-dimensional structures. AIP Adv. 4(2), 1–8 (2014). https://doi.org/10.1063/1.4866170. Article ID 027110 20. L.J. Dong, W.W. Shu, X.B. Li, G.J. Han, W. Zou, Three dimensional comprehensive analytical solutions for locating sources of sensor networks in unknown velocity mining system. IEEE Access 5, 11337–11351 (2017) 21. L.J. Dong, W. Zou, X.B. Li, W.W. Shu, Z.W. Wang, Collaborative localization method using analytical and iterative solutions for microseismic/acoustic emission sources in the rockmass structure for underground mining, Eng. Fract. Mech., https://doi.org/10.1016/j.engfracmech. 2018.01.032 22. L.J. Dong, D.Y. Sun, X.B. Li, K. Du, Theoretical and experimental studies of localization methodology for AE and microseismic sources without pre-measured wave velocity in mines. IEEE Access 5, 16818–16828 (2017) 23. D.M. Morrison, Rockburst research at Falconbridge’s Strathcona Mine, Sudbury, Canada. Pure Appl. Geophys. 129(3-4), 619–645 (1989) 24. L. Linzer, A relative moment tensor inversion technique applied to seismicity induced by mining. Rock Mech. Rock Eng. 38(2), 81–104 (2005) 25. L.J. Dong, D.Y. Sun, X.B. Li, J. Ma, L.Y. Zhang, X.J. Tong, Interval non-probabilistic reliability of surrounding jointed rockmass considering microseismic loads in mining tunnels. Tunn. Undergr. Sp. Tech. 81, 326–335 (2018)

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26. D. Pastén, R. Estay, D. Comte, J. Vallejos, Multifractal analysis in mining microseismicity and its application to seismic hazard in mine. Int. J. Rock Mech. Min. Sci. 78, 74–78 (2015) 27. D.A. Beck, B.H.G. Brady, Evaluation and application of controlling parameters for seismic events in hard-rock mines. Int. J. Rock Mech. Min. Sci. 39, 633–642 (2002) 28. L.J. Dong, W.W. Shu, D.Y. Sun, X.B. Li, L.Y. Zhang, Pre-alarm system based on real-time monitoring and numerical simulation using internet of things and cloud computing for tailings dam in mines. IEEE Access 5, 21080–21089 (2017) 29. H.P. Xie, Y. Ju, F. Gao, M. Gao, R. Zhang, Groundbreaking theoretical and technical conceptualization of fluidized mining of deep underground solid mineral resources. Tunn. Undergr. Sp. Tech. 67, 68–70 (2017) 30. I.G. Main, L. Li, J. Mccloskey, M. Naylor, Effect of the sumatran mega-earthquake on the global magnitude cut-off and event rate. Nat. Geosci. 1(3), 142–142 (2008) 31. Z. He, A discussion of the minimum magnitude of completeness for the northern portion of the north-south earthquake belt of China. J. NW Univ. (Nat. Sci. Ed.) 24, 411–416 (1994) 32. J.B. Rundle, Derivation of the complete Gutenberg-Richter magnitude-frequency relation using the principle of scale invariance. J. Geophys. Res. Solid Earth 94(B9), 12337–12342 (1989) 33. H.E. Hurst, Long-term storage of reservoirs: an experimental study. Trans. Am. Soc. Civil Eng. 116, 770–799 (1951) 34. M. Gilmore, C.X. Yu, T.L. Rhodes, W.A. Peebles, Investigation of rescaled range analysis, the Hurst exponent, and long-time correlations in plasma turbulence. Phys. Plasmas. 9(4), 1312– 1317 (2002) 35. C.G. Bufe, D.J. Varnes, Predictive modeling of the seismic cycle of the Greater San Francisco Bay Region. J. Geophys. Res. Solid Earth 98(B6), 9871–9883 (1993) 36. D.J. Brehm, L.W. Braile, Intermediate-term earthquake prediction using the modified time-tofailure method in Southern California. B. Seismol. Soc. Am. 89(1), 275–293 (1999) 37. Y. Benzion, V. Lyakhovsky, Accelerated seismic release and related aspects of seismicity patterns on earthquake faults. Pure Appl. Geophys. 159(10), 2385–2412 (2002)

A Method for Leak Detection of Spacecraft in Orbit Based on Beam-Forming Lei Qi, Lichen Sun, Donghui Meng, Yong Wang, Wei Sun, and Rongxin Yan

Abstract With the increasing number of space debris, the probability of the collision between the spacecraft in orbit and space debris significantly increases, threatening the stable operation of the manned spacecraft and the safety of the astronauts. So, it is of great significance to the leak detecting of spacecraft in orbit. In this paper, beam-forming algorithm will be used to detect and locate the continuous leak signal, using an L-shape sensor array. By carrying out experiments on an 1 m
1 m
2.5 mm size plate (5A06), the influence of distance between sensor array and the leak source on the accuracy of leak location will be studied both in theory and experiment. The experimental result will be a reference for distribution of sensor array when this method is used in practical application. Keywords Continuous leak · Beam-forming · Distance · Location

1 Introduction With the progress of space science technology and the development of the manned space engineering, the quantity of spacecraft in orbit is growing [1–3]. The spacecraft is subjected to the test of space environment for a long time. It is important to strengthen the security protection in order to guarantee the normal operation of the spacecraft and the life safety of the astronauts. When the cabin of spacecraft is damaged, it is possible that the gas leak will occur which can result in the failure of the sealed structure and the serious loss of life and property. Therefore, the detection and the location of the leak are particularly important [1, 4]. For gas leak detection, the pressure-drop method is widely L. Qi (*) Beijing Institute of Spacecraft Environment Engineering, Beijing, China State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China L. Sun · D. Meng · Y. Wang · W. Sun · R. Yan Beijing Institute of Spacecraft Environment Engineering, Beijing, China © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_39

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accepted. When the air pressure in the cabin changes, leak occurs. But this method cannot locate the source of the leak. Another common method is the helium mass spectrometer leak detection. This method is based on the principle of mass spectroscopy, using helium as a leak detecting gas. This method has the advantages in quantitative detection of leak rate and wide measurement range and is especially suitable for small leak detection. But because of the heavy equipment and the complex process, this method is not suitable for leak detection of spacecraft in orbit. In this paper, beam-forming algorithm will be used to detect and locate the continuous leak signal. The leak signal propagates on the thin wall known as Lamb wave. Lamb wave can propagate far with a stable characteristic, which is more suitable for structural monitoring and leak detection.

2 Location Method Beam-forming method is a relatively perfect acoustic detection algorithm [5]. It mainly uses phased sensor array and utilizes the delay-and-sum (DAS) algorithm to deal with the signal. Beam-forming method is maturely used in radar detection, earthquake wave detection, and communication satellite [6]. In beam-forming algorithm, signals of each element from a certain geometric array are made of a time delay, weighted and added to form the directivity in the predetermined direction [7]. In practice, the acoustic signal generated by the same leak source is continuous and stable, and the leak location can be obtained by combining multiple sets of results from sensor arrays oriented at different positions, as shown in Fig. 1. Get the value of θ1 and θ2 and make a cross, and then the crossing point is the leak source [8]. In practice location, an L-shape sensor array is used (shown in Fig. 2). With the Fig. 1 Locating principle diagram

Sensor array 1

θ1

Leak hole

Sensor array 2

θ2 (x2,y2)

(x,y)

(x1,y1)

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leak N+m

Sound wave surface

ΔtN+m

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θ N

Δt3

c

1

2

3

a

Δt2

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Fig. 2 L-shape sensor array

same number of sensors, the L-shape sensor array can obtain the highest directional accuracy against X-shape sensor array and O-shape sensor array. In Fig. 2, N + m represents the total amount of sensors in the array, where N and m are the number of sensors in the horizontal and vertical directions, respectively; a represents the center-to-center distance between two equally spaced sensors; c is the sound velocity; θ represents the angle between the leak direction and the reference direction. According to the principle of beam-forming, in the algorithm of this paper, sensor 1 is used as a reference sensor, and the signals of No. 2 to No. 8 sensor are timedelayed according to their position. Then the signals are added to calculate the energy. Suppose the signal received by No. i sensor is Ψi ¼ αðRÞ  Ψðt  Δt i Þ þ

X

ψ m , ði ¼ 1; 2; . . . ; 8Þ

ð1Þ

m

Ψ represents the signal at the source of the leak, and

P m

ψ m represents the reflected

wave signal; Δti represents the delay relative to the reference sensor (No. 1 sensor), Δt1 ¼ 0; α(R) is the attenuation factor associated with distance. The distance difference between No. i sensor (i ¼ 2, 3, . . ., 8) to signal source and No. 1 sensor to signal source is di, c represents the wave velocity, and c ¼ 3266 m/s in the magnesium aluminum alloy plate. So time-delay

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Fig. 3 The diagram of power and degree

Δt i ¼

di , ði ¼ 2; 3; . . . ; 8Þ c

ð2Þ

Between d i ¼ ði  1Þ  a  cos θ, ði ¼ 2; 3; 4Þ

ð3Þ

di ¼ ði  4Þ  a  sin θ, ði ¼ 5; 6; 7; 8Þ

ð4Þ

The signals of the No. 1–8 sensors are added to amplify the coherent signal and to reduce the noncoherent signal. The energy of the added signal is PðR; θÞ ¼

8 X i¼1

" αðRÞ  Ψðt  Δt i Þ þ

X

# Ψm

ð5Þ

m

When P(R, θ) takes the maximum value, θ is angle of the leak source direction. Take Fig. 3 for example, the angle of leak source direction is 8 .

3 Experiment Device The experimental system includes a test plate with stiffener, a vacuum pump, a digital 16-channel recorder (DS-16A), an L-shape sensor array, a computer, and so on, shown in Fig. 4. The test plate is square (1000
1000 mm) and 2.5 mm thick. Three different sizes (1.0, 1.5, and 2.0 mm in diameter) of leakage holes have been made randomly on the plate. A vacuum pump with a vacuum nozzle provides the pressure difference between the both sides of the leakage. By starting the vacuum

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Fig. 4 Experimental system

pump, air is drawn off from the vacuum nozzle, leak occurs, and leak acoustic signal is generated. The acoustic signal has been acquired using the sensor array and the digital 16-channel recorder (DS-16A). The sensor array is composed of eight Nano30 sensors with a 125–750 kHz frequency range. In order to amplify the amplitude of signal, eight preamplifiers (gain set to 40 dB) are installed between the sensor array and the recorder. All the data is sent to a computer and processed with the MATLAB® software. Moreover, the frequency of environmental noise is below 20 kHz. Thus, in order to avoid the interferences of environmental noise, the 20–300 kHz frequency band is selected at a sampling rate of 3 MHz.

4 Experiment Data Analysis According to Eq. (5), the directional angle is affected by distance between the source and the array. A leak location experiment is made at different distances. The distance starts from R ¼ 1 cm, and the sensor array moves far away the leakage hole (1.0 mm in diameter) at intervals of 1 cm when the angle is always 45 . Due to the size limitation of the experiment plate, the maximum R is 68 cm. The relationship between directional error and distance (R) is shown in Fig. 5. The leak acoustic signal can be regarded as parallel wave when R > 4 cm based on Mailloux’s empirical formula [9]. When R > 4 cm, the maximum error reached 3.9 . In further analysis, it can be found that when 4 cm  R  20 cm, there are 94% of directional errors less than 2 ; when 20 cm  R  40 cm, there are 80% of the directional errors less than 2 ; when 40 cm < R  60 cm, there are only 43% of directional errors less than 2 ; and the biggest directional error reaches 3.9 . The statistical results are shown in Table 1.

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Fig. 5 Error-distance (R) relation

35 30

error

e

25 20 15 10 5 0 0

10

20

30 40 distance cm

50

60

70

Table 1 Statistic of error 4–20 cm 20–40 cm 40–68 cm

Error less than 2 94% 80% 43%

Fig. 6 The curves of average error with distance

Error between 2 and 5 6% 20% 57%

Error more than 5 0% 0% 0%

6 5

errror

e

4 3 2 1 0

distance cm

Calculate the average value of the directional errors in different distance range; we get the results in Fig. 6. It can be seen that, with the increase of distance, the average error tends to increase gradually because of signal attenuation and reflection. So, the leak location accuracy based on beam-forming method decreases with increasing distance.

5 Conclusion In order to solve the location problem of continuous gas leak in spacecraft in orbit, this paper introduces a leak location method. The experiments showed that, when 4 cm  R  68 cm, the errors are less than 3.9 and, with the increasing of distance, the location error increases gradually because of signal attenuation and reflection. Thus, a way to greatly improve the accuracy of orientation needs to be further researched.

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Acknowledgment This paper is supported by the National Natural Science Foundation of China (Grant No. U1537109) and the Open Foundation of State Key Laboratory of Precision Measurement Technology and Instrument (Tianjin University) (No. pilab1706).

References 1. J.S. Imburgia, Space debris and its threat to national security: a proposal for a binding international agreement to clean up the junk. Vand. J. Transnat’l L. 44, 589–641 (2011) 2. J.C. Liou, S. Debi, Monthly number of objects in earth orbit by object type. Orbit. Deb. Quart. News 16(1), 8 (2012) 3. J.C. Liou, S. Debi, Increase in ISS debris avoidance maneuvers. Orbit. Deb. Quart. News 16(2), 1–2 (2012) 4. Astronauts Complete Spacewalk to Repair Ammonia Leak, Station Changes Command. http://www.nasa.gov/mission_pages/station/expeditions/expedition35/e35_051113_eva.html. 2013.12.05 5. Z.H.O.U. Yu-chang, The development of on-star beam-forming technology of communication satellite. Space Elect. Tech. 03, 7–15 (2000) 6. S.O.N.G. Xin, A method of time-delay and phase-shift beam-forming technology. Inf. Tech. 09, 85–87 (2005) 7. B.I.A.N. Xu, Z.H.A.N.G. Yu, L.I. Yi-bo, et al., A new method of using sensor arrays for gas leakage location based on correlation of the time-space domain of continuous ultrasound. Sensors 15, 8266–8283 (2015) 8. Z.H.A.N.G. Tao, Z.E.N.G. Zhou-mo, L.I. Yi-bo, et al., Vacuum leak detection based on acoustic emission method. J. Vibrat. Shock 32(24), 164–168 (2013) 9. J.R. Mailloux, Phased Array Antenna Handbook (Artech House Publishers, Norwood, MA, 2005)

Improved Ray Tracing Method Based on the Snell’s Law Qingchun Hu

Abstract Passive wave tomography is one of the important techniques for microseismic monitoring in the complex underground mines. The resolution and reliability of tomography are influenced by the ray tracing method. Compared with other algorithms, the shortest path method is a robust global ray tracing algorithm, but there are still some problems. The study attempts to use the shortest path method to make the ray traced to satisfy the law of wave propagation, making it more realistic. The Snell’s law is introduced to improve the accuracy of the ray tracing method, and the disturbance of the points is considered so that the Snell’s law can modify the ray more effectively. The improved method is used to perform simulation in the grid model, and the result is compared with the traditional ones. The results show that the improved method combines the advantages of various methods and achieves good results, which indicates that the ray can jump out the original cell and bypass the empty area. The improved ray tracing method also can get a global optimal solution. Keywords Ray tracing · The Snell’s law · The shortest path

1 Introduction With the increasing demand for valuable resources and the development of mining technology, deep mining projects are carried out in many countries including South Africa, Canada, Australia, America, Poland, and China [1–5]. Microseismic monitoring is the main means of disaster prevention in deep mines [6], in where a large number of disasters occur [7]. Passive wave tomography is one of the important techniques for microseismic monitoring in the complex underground mine, which requires locating the source before imaging. There are two obvious types of the current localization methods [8]. Through the established nonlinear governing Q. Hu (*) School of Resources and Safety Engineering, Central South University, Changsha, China e-mail: [email protected] © Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2_40

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equations for a microseismic event, the source coordinates can be solved according to the derived explicit analytical formulas, which is exactly the main thought of the analytical localization method [9, 10], such as the 3D analytical solutions without predetermined P-wave velocity [11–13]. Another is the iterative localization method, which is more applicable to the deep mining environment with dynamic disturbances [10]. With the tomography technology, we can use the source location and the projection data obtained by external detection equipment to intuitively and clearly display the fine structure and local inhomogeneity of the object in the form of images (such as using the P-wave arrival time received by acoustic emission to reconstruct the wave velocity structure inside the object). The wave tomography technology is mainly composed of forward modeling and inversion. Forward modeling generates ray paths and travel times in a prior velocity model [14], whose calculation accuracy and calculation speed directly determine the resolution and reliability of imaging. It includes dynamic methods based on wave equations and kinematic methods based on ray theory. The result of the dynamic method is accurate since the wave equation contains all the information of the wave field. However, the dynamic method will extend to the inverse problem of the partial differential equation in the imaging process. Therefore, the large-scale three-dimensional numerical calculation and strict boundary conditions are required to solve the problem. The ray tracing method is the main forward method currently, which does not need to solve the related problems of the partial differential. It is highly efficient and capable of recovering velocity structures in the complex discontinuous material. Traditional methods of ray tracing between the source and the detector can be classified into shooting and bending. Shooting determines the ray path by correcting the exit angle of the ray constantly to reach the detector. It is based on Snell’s law propagated with good accuracy in layered media, whereas there are cases where a ray path cannot be found in a 2D or 3D mesh media. The bending method works with an initial ray path and continuously optimizes the ray according to the minimum travel time principle. Gao Er-gen [15] proposed a new kind of step-by-step iterative ray tracing method based on the Snell’s law to improve the accuracy of the bending method effectively. Nevertheless, it may only be a local optimal solution, not a global optimal solution. A large number of new algorithms have been proposed in consideration of the shortcomings of the traditional methods, including the finite difference method [16–18], the travel time interpolation method [19–21], the wave front method [22–24], the shortest path method [25–28], etc. However, there are still some problems with these methods, for example, first, it is general with the characteristics of the poor adaptability to complex areas, especially in areas with large differences in wave speed; second, the accuracy of the model is poor, and the error will continue to accumulate with ray tracing; and third, it is difficult to solve with a large amount of calculation. Among these algorithms, the shortest path method is a robust global algorithm, which pioneered to ray tracing by Nakanishi et al. [25] in 1986. It is applicable to 2D and 3D imaging of complex interior structures and has no blind spots. The shortest path method comes from graph theory to calculate the shortest path from one node to

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all other nodes. Because of the limitation of its own method, the ray path obtained by the shortest path method is serrated and does not satisfy Snell’s law. And the ray path is prone to multi-value problems in cases where the variation of wave velocity is not significant. Many scholars endeavor to solve the above problems and try to improve the algorithm’s computing efficiency, such as Moster [26], Klimeš [27], Wang [28], etc. In this paper, we improve the accuracy of the shortest path method by introducing the Snell’s law and adding the perturbation. We use the improved method to simulate in the grid model and compare the result with the traditional one.

2 Methods The shortest path method can give a path between two points with global solution, while it has the problem with relatively low accuracy. The iterative ray tracing method based on the Snell’s law, which can modify the ray step by step, suffers from the problem of obtaining the path with local optimal solution and trapping in the cell, though it generally has high precision in the local path. We attempt to combine the advantages of shortest path method and Snell’s law, to improve the algorithm. First, the shortest path method is used to get the initial path, and the perturbation is added to make the path be adjusted to suit the Snell’s law. Then, the second tracking is based on the first path.

2.1

The Shortest Path Method

In graph theory, the shortest path method is the classical algorithm of finding a path between two points such that the distance is minimized. In other words, this method is to solve the topology and combination problems of the one-dimensional line, to find its optimal solution. It is common to use Dijkstra’s algorithm to get the optimal solution of the global path [29] because Dijkstra’s algorithm is a typical algorithm for solving the shortest path, which is used as an example in many tutorials. It was proposed by Dutch computer scientist Dijkstra in 1959 to find the global optimal path from the starting point to all other points [30]. The main characteristic of this algorithm is centering on the starting point, adopting the idea of a greedy algorithm and expanding to the outer layer until it reaches the end point. This paper adopts the Dijkstra’s grid model as shown in Fig. 1. There are four paths from the point inside the cell to the nodes of the corner, five paths from the node of the edge to other nodes, and normally eight paths from one node to another. Let the node at which we are starting be called the initial node. Let the distance of node Y be the distance from the initial node to Y. Dijkstra’s algorithm will assign some initial distance values and will try to improve them step by step.

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Fig. 1 Grid model of node propagation

=

=

=

&&

=

=

=

=

&&

=

Fig. 2 No disturbing in four special conditions

According to the initial path obtained by the Dijkstra’s algorithm, we should consider the following cases and judge if it needs to amend the ray.

No Disturbing When the above four special conditions appear, shown in Fig. 2, there is no need to disturb the intermediate point (P2).

Comparing the Velocity When the x2 only equals to x3 or the y1 only equals to y2 or the x1 only equals to x2 or the x1 only equals to x2, we should compare the velocity of the yellow area (Vyellow) to the velocity of the green area (Vgreen), shown in Fig. 3. When Vyellow
Vgreen, we choose the Ray1. When Vyellow < Vgreen, we should compare the travel time of the Ray1 to the Ray2 and choose the less one.

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Fig. 3 Comparing the velocity in four conditions

2.2

Snell’s Law

Snell’s law can be used to describe the propagation direction of the wave front, when referring to waves passing through a boundary between two different isotropic media [31]. Snell’s law states that the ratio of the sines of the angles of incidence and refraction is equivalent to the ratio of phase velocities in the two media: sin θ1 sin θ2 ¼ v1 v2

ð1Þ

where each θ as the angle measured from the normal of the boundary and v as the velocity of light in the respective medium. The Snell’s law is used to constrain when the points in the ray are disturbed. When the interface is in the x-axis direction, it is easy to obtain the formula: v1 ðx3  x2  dÞ v2 ðx2  x1 þ dÞ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðx3  x2  dÞ2 þ ðy3  y2 Þ2 ðx2  x1 þ dÞ2 þ ðy2  y1 Þ2

ð2Þ

Change the second point of the coordinates: x2 ¼ x2 + d y2 ¼ y2 When the interface is in the y-axis direction, we also can get the equation: v1 ðy3  y2  dÞ v2 ðy2  y1 þ dÞ ffi ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð x3  x2 Þ 2 þ ð y3  y 2  d Þ 2 ð x2  x1 Þ 2 þ ð y2  y 1 þ d Þ 2

ð3Þ

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Fig. 4 Disturbing three points

p3(x3,y3)

d

θ2 v2

p2(x2,y2)

p’2(x’2,y’2)

θ1

v1

p1(x1,y1) Change the second point of the coordinates: x2 ¼ x2 y2 ¼ y2 + d We can get the d by using analytical methods or numerical methods. In this paper, we solve it by dichotomy. In order to get the local optimization of the ray, we use the Snell’s law to disturb the path nodes in turn [15], as shown in the Fig. 4. The core idea is: 1. First, remove the nodes of the initial path and sort them on the X-axis (also available on the Y-axis) coordinate. 2. Then, modify the ray based on three points (P1, P2, P3). By disturbing the intermediate point (P2) based on Snell’s law to get the d, we can move the point (P2) to (P02 ) and alter the curve P1 P2 P3 to P1 P02 P3 . 3. Then, P02 P3 P4 will be altered, followed by modifying the rest until the correction is completed. 4. Finally, the disturbance optimizing can be repeated to achieve the preset accuracy. There are still defects in the algorithm. The rays cannot jump out of the cell as long as the rays pass through the cell. The further disturbance will be carried out to solve the limitations to make the tracing ray continue to be modified by Snell’s law. We consider the following two cases in particular:

Rays Pass Through Nodes (as Shown in Fig. 5) If there is v2 > v4, the polyline will pass through the No. 2 area. The coordinates of y2 þy3 2 the two nodes are x1 þx 2 , y2 and x2 , 2 , respectively. Otherwise, the polyline will pass through the No. 4 area. And the coordinates of x2 þx3 2 the two nodes are x2 , y1 þy 2 and 2 , y2 , respectively.

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Fig. 5 Rays pass through nodes

P1

v1

v2 P2

v4

P1

v1

v2

P1

v1

P2 P3 P2 ’ P3’ v4

v3

P3

v2 P2’ P3’ P2 P3

v3

P4

v4

v3

P4

Fig. 6 P2 and P3 are close to the same node

P2 and P3 Are Close to the Same Node As shown in the Fig. 6, we can do as follows when the two tracking points are very close to the same node. If the ray passes through area 2 and v4 > v2, then P2 and P3 will be modified to 2x2 – x3, y3 and x2, 2y3,  y2 temporarily. If the ray passes through area 4 and v2 > v4, then P2 and P3 will be modified to x3, 2y2,  y3 and 2x3 – x2, y2 temporarily. The path is finally identified according to the travel time of P1 P2 P3 P4 and P1 P02 P03 P4 . Without the limitation of their own existence, the new algorithm is more precise and obtains the tracing ray more realistic.

3 Simulation Calculation First, we can build a simple plate model which can calculate the ideal ray easily. Comparing with the ideal ray, we can evaluate the improvement method. As shown in Fig. 7, the green of the middle part is the empty area ABCD (10 m  x  20 m,

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Fig. 7 The plate model

5 m  y  20 m), in which the wave velocity is 500 m/s. The wave velocity in the surrounding area is 3500 m/s. We can get the ray from the point (0, 30) to the point (30, 0) and mark the ray as No. 1 Ray. If the ray passes through the empty area ABCD: 1. When the ray passes through AB and BC, the travel time is:

T¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðx  0Þ2 þ ð30  20Þ2

þ

þ 3500 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð20  30Þ2 þ ðy  0Þ2

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðx  20Þ2 þ ð20  yÞ2 500 ð4Þ

3500

Tmin ¼ 0.0127775313 s, when the x ¼ 20 and y ¼ 20. 2. When the ray passes through AD and BC, the travel time is:

T¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð0  10Þ2 þ ð30  y1 Þ2

þ

þ 3500 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð20  30Þ2 þ ðy2  0Þ2 3500

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð10  20Þ2 þ ðy1  y2 Þ2 500 ð5Þ

Tmin ¼ 0.03194414052 s, when the y1 ¼ 17.805 and y2 ¼ 12.195. 3. When the ray passes through AD and DC, the minimal travel time is longer than the minimal travel time of the ray passes through AB and BC. 4. When the ray passes through AB and DC, the minimal travel time is longer than the minimal travel time of the ray passes through AD and BC.

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Fig. 8 The ideal paths

No.1 Ray No.2 Ray

No.3 Ray

So, if the ray passes through the empty area ABCD, the minimal travel time is: Tmin ¼ 0.0127775313 s Otherwise, the ray does not pass through the low-speed area ABCD. The minimal travel time is also equal to 0.0127775313 s when the ray passes through the point (20, 20). In summary, the minimal travel time between the point (0, 30) to the point (30, 0) is equal to 0.0127775313 s. Similarly, we get the second ray from the point (0, 25) to the point (30, 5) and mark it as No. 2 ray; get the third ray from the point (0, 12.5) to the point (30, 12.5), and mark it as No. 3 ray, as shown in Fig. 8. The travel time of No. 2 ray is 0.011040938 s, and travel time of No. 3 ray is 0.010000000 s. We regard these three rays as ideal paths to test the accuracy of ray tracing methods.

3.1

The Traditional Path

We cannot use the shortest path method directly to seek the shortest path in the 2D plane, because of the lack of nodes. Therefore, it is necessary to perform grid discretization on the velocity plane to get the corner points of the cell. The grid points are the nodes we need, which can be connected to form the Dijkstra’s grid model. After the grid is discretized, it is time to trace the rays through Dijkstra’s algorithm. Programming with Cþþ, we also obtain three rays separately from the point (0, 30) to the point (30, 0), the point (0, 25) to the point (30, 5), and the point (0, 12.5) to the point (30, 12.5). It is typical that the rays have the serrated shape, as shown in the Fig. 9a. It is obvious that the rays are not fit with the ideal paths, and the calculation error of the travel time is continuing to accumulate as waves propagate.

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Fig. 9 The paths of ray tracing

We tried to solve the limitations of the shortest path method to own existence to introduce the Snell’s law, making the tracing ray more realistic. Before applying the Snell’s law to the shortest path algorithm, it is essential to know the shortage of the Snell’s law in addition to only local optimization. We employed the traditional ray tracing method based on the Snell’s law and program with Cþþ using dichotomy to gain the paths in order to better understand the lack, as shown in the Fig. 9b. From the figure, we can find other two problems: 1. If the ray passes through the node, the Snell’s law would be invalid to fix the ray. 2. The ray is not modified when the ray is perpendicular to the grid. The first problem can be worked out by the disturbance, and the point of the ray in the node can be moved. The second problem will not exist when the initial path is obtained by the shortest path method.

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3.2

439

The Improved Path

The ray tracing method is improved by combining the shortest path method, the Snell’s law, and the disturbance. First, the Dijkstra’s algorithm, which is able to find the global optimal solution, is used to track the initial ray. Then, the algorithm of ray tracing based on Snell’s law is used to correct the initial rays. Of course, the disturbance is adopted to make the Snell’s law continue to modify the rays, and the rays can jump out of the grid. The final result of ray tracing is shown in Fig. 9c. The results show that the new method combines the advantages of the multiple methods and performs well. It is obvious that all tracing rays can bypass the ultralow speed zone and jump out the original cell.

4 Result Analysis In wave velocity tomography, elastic waves are propagated in the medium in the form of rays. The total travel time of the ray is equal to the sum of the travel time of the ray, passing through each cell in the grid. The velocity grid model is known, and the path can be determined according to the ray tracing method. Therefore, the total travel time of each ray can be worked out: Tk ¼

X Δs j vj j

ð6Þ

where Tk is the total travel time of the k ray, Δsj is the travel distance by the k ray in the j cell, and vj is the wave velocity in the j cell. We calculated the travel time of each path got by different ray tracing methods according to the velocity grid model of each cell. The travel times of the No. 1, No. 2, and No. 3 rays are shown in Table 1. In general, the smaller relative error indicates the better reliability of data. By comparing the travel times with the ideal path to the others, the relative error (θ) can be obtained easily according to Eq. (7). θ¼

T kP  T 0k  100% T 0k

ð7Þ

where each T 0k as the travel time of the ideal paths, N as the number of the rays. each T kP as the travel time of the other paths, k as the number of the rays, and P as the kind of the paths. Comparing the relative error of the travel times from the Table 2, it is obvious that the rays obtained by the improved method perform well. The relative error of the travel times of these rays is very small because they are nearly close to the ideal paths.

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Table 1 Travel times (unit: s) The Dijkstra The Snell The disturbed The improve The ideal Table 2 Relative errors (compare with the ideal path. Unit: %)

No. 1 ray 0.013795506 0.036365492 0.030300175 0.012781544 0.012777531

The Dijkstra The Snell The disturbed The improve

No. 2 ray 0.011775201 0.027981531 0.028091447 0.011043162 0.011040938

No. 1 ray 7.97 184.60 137.14 0.03

No. 3 ray 0.011183467 0.025714286 0.025714286 0.010028618 0.010000000

No. 2 ray 6.65 153.43 154.43 0.02

No. 3 ray 11.83 157.14 157.14 0.29

Moreover, because the shortest path method can bypass the ultralow speed zone automatically in the time of ray tracing, the shortest path method has superior effectiveness compared to the bending method that is based on the Snell’s law in the complex interior structures. The disturbance is useful for the method based on the Snell’s law as a supplement, particularly in the case when the ray passes through more than one node of the grid.

5 Discussion and Conclusion As we all know, the ray traced by the traditional method based on the Snell’s law cannot jump out the cell in the velocity grid model and only find the path on the basis of the local optimization. It means that the initial path will directly affect the accuracy of the tracking ray. An abnormal result may appear: the accuracy of the tracking ray is lower as the grid is finer, especially in the discontinuous areas where the wave velocity varies greatly. It’s also a tricky problem that the Snell’s law will be invalid to fix the ray when the ray passes through the node or is perpendicular to the grid. The shortest path method also has an obvious shortage that the tracing rays are not fit with the ideal paths and the calculation error of the travel time is continuing to accumulate as waves propagate. A number of measures are taken to alleviate the problem, but the cost is to add more nodes and greater calculations. In this paper, we combined a set of methods, including the bending based on the Snell’s law, the shortest path method, and the point disturbing. We used the improved method to perform simulation in the grid model and compare the results with the traditional ones. The results show that the new method combines the advantages of these methods and solves the problems of the shortest path method and the bending method based on the Snell’s law. It indicates that the ray can jump

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out the original cell and bypass the low-speed area. The improvement of the ray tracing method also can get a global optimal solution. This paper only considers the case with large differences in wave speed, and more detailed research on this improved method is needed to conduct. If we require a better path, we can also carry out the following measures. It is the most common and direct method that we can densify the grid. Of course, the phenomenon that the accuracy is lower as the grid is finer will never appear, though it adds more nodes and greater calculations. Therefore, it is better to amend the grid model of nodes, which is more suitable for the improved method. Acknowledgment The authors wish to acknowledge financial support from the Fundamental Research Funds for the Central Universities of Central South University (2018zzts722), The Young Elite Scientists Sponsorship Program by CAST (YESS20160175).

References 1. L.J. Dong, J. Wesseloo, Y. Potvin, X.B. Li, Discriminant models of blasts and seismic events in mine seismology. Int. J. Rock Mech. Min. Sci. 86, 282–291 (2016) 2. L.J. Dong, J. Wesseloo, Y. Potvin, X.B. Li, Discrimination of mine seismic events and blasts using the Fisher classifier, naive Bayesian classifier and logistic regression. Rock Mech. Rock Eng. 49(1), 183–211 (2016) 3. S. Lasocki, B. Orlecka-Sikora, Seismic hazard assessment under complex source size distribution of mining-induced seismicity. Tectonophysics 456(1), 28–37 (2008) 4. M.K. Abdul-Wahedab, M. Al Heiba, G. Senfaute, Mining-induced seismicity: Seismic measurement using multiplet approach and numerical modeling. Int. J. Coal Geol. 66(1-2), 137–147 (2006) 5. A. Leśniakab, Z. Isakow, Space–time clustering of seismic events and hazard assessment in the Zabrze-Bielszowice coal mine, Poland. Int. J. Rock Mech. Min. Sci. 46(5), 918–928 (2009) 6. L.J. Dong, D.Y. Sun, X.B. Li, J. Ma, L.Y. Zhang, X.J. Tong, Interval non-probabilistic reliability of surrounding jointed rockmass considering microseismic loads in mining tunnels. Tunn. Undergr. Sp. Tech. 81, 326–335 (2018) 7. J. Ma, L.J. Dong, G.Y. Zhao, X.B. Li, Discrimination of seismic sources in an underground mine using full waveform inversion. Int. J. Rock Mech. Min. Sci. 106, 213–222 (2018) 8. L.J. Dong, W. Zou, X.B. Li, W.W. Shu, Z.W. Wang, Collaborative localization method using analytical and iterative solutions for microseismic/acoustic emission sources in the rockmass structure for underground mining, Eng. Fract. Mech., https://doi.org/10.1016/j.engfracmech. 2018.01.032 9. R. Duraiswami, D. Zotkin, L. Davis, Exact solutions for the problem of source location from measured time differences of arrival. J. Acoust. Soc. Am. 106(4), 2277 (1999) 10. L.J. Dong, W.W. Shu, X.B. Li, G.J. Han, W. Zou, Three dimensional comprehensive analytical solutions for locating sources of sensor networks in unknown velocity mining system. IEEE Access 5, 11337–11351 (2017) 11. L.J. Dong, X.B. Li, Three-dimensional analytical solution of acoustic emission or microseismic source location under cube monitoring network. Trans. Nonferrous Met. Soc. Chin. 22(12), 3087–3094 (2012) 12. L.J. Dong, X.B. Li, Z.L. Zhou, G.H. Chen, J. Ma, Three-dimensional analytical solution of acoustic emission source location for cuboid monitoring network without pre-measured wave velocity. Trans. Nonferrous Met. Soc. Chin. 25(1), 293–302 (2015)

442

Q. Hu

13. X.B. Li, L.J. Dong, An efficient closed-form solution for acoustic emission source location in three-dimensional structures. AIP Adv. 4(2), 1–8 (2014) 14. X.H. Yang, J.S. He, D.Q. Xie, The forward and inversion technology for velocity tomography. Geophys. Geochem. Explor. 33(2), 217–219 (2009) 15. G. Ergen, X. Guoming, A new kind of step by step iterative ray-tracing method. Chin. J. Geophys. 39(Suppl), 302–308 (1996) 16. J.E. Vidale, Finite-difference calculation of travel times. Bull. Seism. Soc. Am 78(6), 2062–2076 (1988) 17. J.E. Vidale, Finite-difference calculation of travel times in three dimensions. Geophysics 55(5), 521–526 (1990) 18. F. Qin, Y. Luo, K.B. Olsen, W. Cai, G.T. Schuster, Finite-difference solution of the eikonal equation along expanding wavefronts. Geophysics 57(3), 478–487 (1992) 19. E. Asakawa, T. Kawanaka, Seismic ray tracing using linear traveltime interpolation. Geophys. Prospect. 41(1), 99–111 (1993) 20. E. Cardarelli, A. Cerreto, Ray tracing in elliptical anisotropic media using the linear travel time interpolation (LTI) method applied to travel time seismic tomography. Geophys. Prospect. 50 (1), 55–72 (2002) 21. N. Jianxin, Y. Huizhu, Quadratic/linear travel time interpolation of seismic ray-tracing. J. Tsinghua Univ. (Sci. Tech.) 43(11), 1495–1498 (2003) 22. N. Ettrich, D. Gajewski, Wave front construction in smooth media for prestack depth migration. Pure Appl. Geophys. 148(3-4), 481–502 (1996) 23. K.J. Lee, R.L. Gibson, An improved mesh generation scheme for the wavefront construction method. Geophysics 72(72), 59–70 (2007) 24. V. Vinje, E. Iversen, H. Gjoystdal, Travel time and amplitude estimation using wavefront construction. Geophysics 58(8), 1157–1166 (1992) 25. I. Nakanishi, K. Yamaguchi, A numerical experiment on nonlinear image reconstruction from first-arrival times for two-dimensional island arc structure. Earth Planets Space 34(2), 195–201 (1986) 26. T.J. Moser, Shortest path calculation of seismic rays. Geophysics 56(1), 59–67 (1991) 27. L. Klimeš, Kvasnička and Michal. “3-D network ray tracing,”. Geophys. J. Int. 116(3), 726–738 (1994) 28. W. Hui, C. Xu, 3-D ray tracing method based on graphic structure. Chin. J. Geophys. 43(4), 534–541 (2000) 29. T.H. Cormen, C.E. Leiserson, R.L. Rivest, Stein and Clifford. Section 24.3: Dijkstra’s algorithm, in Introduction to Algorithms, 2nd edn., (MIT Press, McGraw–Hill, Cambridge, MA; Boston, MA, 2001), pp. 595–601 30. E.W. Dijkstra, A note on two problems in connection with graphs. Numer. Math. 1(1), 269–271 (1959) 31. R.J. Schechter, Snell’s Law: optimum pathway analysis. Surv. Ophthalmol. 21(6), 464–466 (1977)

Index

A Acoustic emission (AE) sensor, 3–4 arrival time and energy for signal, 218, 221 calibration of sensors, 10–11 with different materials. 39–41 elastic wave source, 3–5 installation of, 235 at low frequency calibration, 4, 6, 17 pipeline leak location theory, 306–307 secondary calibration, 10–11 steel sheet breaking test, 41 with tensile specimen, 178–179 testing of ceramic matrix composite, 216–217 Acoustic pressure and velocity, 357 Acrylic encapsulation FBG sensor, 39–40 Alloy structural steel acoustic emission, 206 conventional nondestructive testing, 205, 206 experiment mechanical characteristics analysis, 209–211 parameter setting and loading scheme, 208–209 specimen processing and finite element simulation, 208 tensile damage characteristics, 211–213 medium-sized machinery manufacturing industry, 205 metallic material elastic deformation stage, 206, 211–212 hardening stage, 207, 213 necking fracture stage, 207, 213 plastic deformation stage, 206–207, 213

Aluminum alloy pressure vessels, in friction stir welding method blasting fracture morphology, 349–350 loading process, 342–343 manufacture, 341–342 test process, 342–343 American Physical Acoustics Corporation (PAC), 380 AmpDif mean values, 96 Amplitude modification curve, 324–325 AMSY-5 model, 342 Amusement device crack propagation analysis, 267 large-scale, 259 magnetic particle testing, 260 steel structure, 259–260 testing object and equipment, 260–261 U-shaped track attenuation characteristics, 261–262 location analysis, 265–267 parameter characteristics, 263–264 sound velocity test, 262–263 waveform and spectrum characteristics, 264–265 Analysis of variance (ANOVA), 361 Analytical localization method, 400 Antisymmetric Lamb waves, 88 Artificial Intelligence technique, 132 Attenuation characteristics of steel bifurcated pipe, 323–325 of U-shaped track, 261–262 Automatic encoder, 328–330Automatic multichannel monitoring system, 410 Axle crack, 108, 110, 112, 113, 116 Axle fatigue test, 108

© Springer Nature Switzerland AG 2019 G. Shen et al. (eds.), Advances in Acoustic Emission Technology, Springer Proceedings in Physics 218, https://doi.org/10.1007/978-3-030-12111-2

443

444 B Backpropagation neural network (BPNN), 328 Bandpass filtering, 77 Beam-forming method acoustic signal locating principle, 422 center-to-center distance, 423 experiment data analysis, 425–426 device, 424–425 i sensor, 423 L-shape sensor array, 422–423 power and degree, 424 uses, 422 Benioff strain calculation method, 413 Biorthogonal wavelet, 368 Blasting fracture morphology, of aluminum alloy container, 349–350 b value, of regional seismicity level, 411

C Carbon fiber-reinforced matrix ceramic (CMC) structures, 215 Carbon/silicon carbide-reinforced composites (C/SiC), 215 CDLNN, see Classification deep learning neural network (CDLNN) Cell evaluation method, 410 Ceramic matrix composite structures energy and arrival time relationship, 218, 221 400 kHz high-pass filter, 217, 220 measurement, 216–217 parameter selection, 216 sensor arrangement, 217–218 spectrum, 219 Classification deep learning neural network (CDLNN) depth learning neural network, 330–331 feature variable selection, 332 model classification performance test, 331–333 pipeline status coding, 332 pipeline troubleshooting procedure, 333–334 sample data selection, 331 Clustering algorithm (k) Coefficient of correlation, 148, 151 Coil turns, 50–54 Concrete slabs b-value, 240, 245–246 experimental condition, 241–242 ib-value analysis, 243–244 improved b-value, 241

Index reinforced concrete bridge decks, 240 road infrastructures, 239 seismic b-value, 241 tomography, 240 velocity distributions, 243 Concrete strength forecasting, 71–72 Continuous HMM (CHMM), 381 Continuous wavelet transform (CWT), 308 Coordinate system, 89 Correlation analysis method correlation coefficient calculation, 107, 113 correlation degree calculation, 107–108, 113 non-dimensional treatment of data, 106–107 sequences determination, 106 weights determination, 107 See also Grey correlation analysis Correlation coefficient, 156, 160–161 Corrosion pits, 92 Counting and energy analysis method, for partial discharge, 390, 393–395 Cracks, 142 Crack signals, 111 Crack stage, 108 Crack state, 116 Crack time, 109 30CrMo steel, 206 Curve fitting, 322, 323

D DaisAE hub, 62 perceived wire break monitoring, 63 See also Power over Ethernet (PoE) daisy chain technology Data acquisition cycle, 44, 45 Deep learning neural network (DLNN), 328 De-noising methods, 166 Digital image correlation (DIC) method, 125, 126, 196 Direct current potential drop (DCPD) method, 169 Discrete hidden Markov chain model (DHMM) acoustic emission, 379 subframe processing, 381 wavelet packet eigenvector extraction, 381–382 basic theory, 380–381 fault diagnosis, 384 implementation, 381–384 logarithmic likelihood probability values, 384, 385 multiple observation sequences training, 382–384

Index results, 384 rolling bearing test platform, 380 rotating machinery, 379 time framing, 381 Dynamic and static loads, of granite fracture modes experimental setup, 143–144 fracture morphology, 145, 147 peak frequency vs. amplitude, 145, 147 RA-value and frequency characteristics, 144–147 sample preparation, 143 waveforms analysis in impact-loading tests, 147–151 Dynamic fatigue tests, 185

E E-glass, 155 Elastic wave, 142 sources, 4–5 tomography, 240 Electron beam free-form fabrication (EBF3), 177–178 Empirical mode decomposition (EMD), 104, 121, 122 Energy entropy, 124–125 Energy modes, 125–128 Energy vector, 122, 124–125 Epilogue, 46–47 Ethylene horizontal tank acoustic emission testing equipment and parameters, 291 inspection standard, 290–291 loading process, 291–295 parameters, 290 penetrant testing, 293 sensor layout scheme, 291 chemical enterprise, 290 pressure vessels, 289 Event rate, 281, 282 Event response loop, 44, 45 Events of random damage (ERD), 189 Extensional mode, 88

F Face-to-face configuration advantage, 13 disadvantage, 13–14 principle, 12–13 Fatigue cycles, 192

445 damage accumulation acoustic emission testing, 184 categories, 184 experimental steps and configuration, 185–186 probability entropy analysis, 188–190 quantification of, 183 scanning electron microscope fractographs, 190–192 specimen preparation, 184–185 static tensile test data analysis, 186–188 statistical approach, 184 damage evaluation amplitude and crack size, 170–171 compact tension specimens, 169 count and crack size, 170–171 de-noising methods, 166 entropy, 168–169 experimental procedures, 169–170 fatigue crack growth, evaluation, 167–168 fracture surfaces, 173–174 normalized cumulated entropy, count, and energy, 173 2.25Cr-1Mo-0.25V steel, 169 parameters, 166–167 Shannon’s entropy, 168 hoop-wrapped composite cylinders cycle signals, 299 experiment, 298–299 initial stage, 299–300 leak stage, 301–302 Fault recognition, 104 Fiber Bragg grating (FBG) AE detecting system setup, 30 AE sensor package, 38–43 central wavelength, 28, 29 and PZT acoustic emission sensors, 25–34 reflected spectrum, 29 sensing principle, 26–28 Fiber Bragg grating (FBG) acoustic emission demodulation system, 76 advantages, 76 broken pencil lead on steel plate test, 77–79 detection method, 75–77 principle, 76–77 rotating machinery fault detection experiment, 79–82 signal processing method, 77 Fiber Bragg grating (FBG) sensor acoustic emission detection system, 37–38 acrylic encapsulation, 39–40 demodulating system theory, 28–29

446 Fiber Bragg grating (FBG) sensor (cont.) with different materials, 39–41 epilogue, 46–47 experiments detecting system setup, 30 direction sensitivity, 30–33 frequency response, 31–33 mechanism, 38–39 performance test, 41–43 principle, 26–28 software design event structure, 44 front panel display, 46 LabVIEW, 43, 44 producer/consumer model, 44 stainless steel package, 40–41 wavelength, 26 Fiber-optic detecting technology, 26 Fiber-optic Fabry-Perot cavity technology, 26 Flexural mode, 88 Fourier transform (FT), 120 Friction stir welding (FSW) method acoustic emission testing amplitude, 343, 345 characteristic parameter analysis method, 343–346 deformation and crack propagation, 345 elastic strain phase, 345 feature parameters, 343–344 parameter distribution characteristics, 346 source location technology, 347–349 stages, 345–346 stress wave emission, 344–345 subcritical crack propagation, 345 aluminum alloy pressure vessels blasting fracture morphology, 349–350 loading process, 342–343 manufacture, 341–342 test process, 342–343 vs. traditional melting welding method, 341 Function generator (FG), 14

G Geometric diffusion attenuation, 322–323 Glass fiber-reinforced plastics (GFRPs) lock-in thermography, 156 multi-delamination, 156 terahertz time-domain spectroscopy, 156 using ultrasonic full-waveform comparison correlation coefficient, 160–161 data processing, 160–161

Index experimental setup, 157–159 received waveforms, 158, 159 reference waveform, 158, 159 transmitted pulse waveform, 157, 158 transmitted signal spectral, 157, 158 Glass fibers, 155 Goodness of fit, 322 Granite fracture modes, under dynamic and static loads experimental setup, 143–144 fracture morphology, 145, 147 RA-value and frequency characteristics, 144–147 sample preparation, 143 waveforms analysis in impact-loading tests, 147–151 Graphics Processing Unit (GPU), 132 Grey correlation analysis correlation coefficient calculation, 107, 113 correlation degree, 107–108, 113 non-dimensional treatment of data, 106–107 sequences determination, 106 validation, 104 weights determination, 107, 112, 115 See also Local mean decomposition (LMD) Grey system theory, 103 Gutenberg-Richter magnitude-frequency relationship, 411–412

H Hardening stage, 207, 213 Hidden Markov model (HMM), 380–381 Hilbert-Huang transform (HHT), 120, 121 Hilbert spectral analysis (HSA), 121 Hoop-wrapped composite cylinders actual leak location, 302, 304 dynamic testing, 298 energy duration and amplitude, 301, 302 fatigue cycle signals, 299 experiment, 298–299 initial stage, 299–300 leak stage, 301–302 filtering method, 301, 303 location of signal, 302, 303 periodical surveys, 297–298 uses, 297 VS900 and VS150-RIC sensor signals, 299 Hurst exponent, Seismic sequence, 412–413

Index I Impact-loading tests correlation coefficient, 148, 151 RA-value for peak frequencies, 148, 149 waveforms analysis in, 147–151 Improved b-value, in concrete slabs, 241 Independent component analysis (ICA), 356 Inner raceway defects (IRD), 250–252, 255, 256 Integral thickness (IT) measurement, 66, 69–71 International Society for Rock Mechanics Commission on 85 Rock Dynamics, 143 International Thermonuclear Experimental Reactor (ITER), 103 INTERUNIS-IT, 65, 66, 69, 71 Intrinsic mode functions (IMFs), 121, 122, 126, 127 Iterative localization method, 400, 430 Iterative ray tracing method, 431

J Jiangsu Special Equipment Safety Supervision Inspection Institute, 290

K Kaiyang phosphorous mine, multistep source localization method, 402–403

L LabVIEW, 43, 44 LabVIEW2010, 43 Lamb wave, 88, 422 propagation, 70 Large corrosion surface (LCS), 91 Laser inferometry, 10 Laser vibrometer (LVM) results, 16–18 setup, 14–16 Leak detection, 276, 305–307, 334, 366, 421–426 Linear location method, 179 Loading effect, 22–23 Local mean decomposition (LMD), 106 algorithm flow chart, 105 crack signals stage, 111 fault recognition flow chart, 104 processing of signals, 110–116 product function, 104 root mean square value, 110, 112 signal process, 110–116 Local necking and fracture stage, 207, 213

447 Lock-in thermography, 156 Low-speed heavy-duty bearing experimental procedure, 251–252 setup, 250–251 information carrier, 250 parametric analysis, 250, 252–253 rotary bearings, 249 rotating speed and load effects, 256 spectrum analysis, 253–255 transient elastic waves, 250 wavelet transform analysis, 250, 255–256 LVM, see Laser vibrometer (LVM)

M Magnetic field strength, 53 Magnetic flux leakage (MFL) test, 284–286 Magneto acoustic emission (MAE) characteristics, 50 definition, 49 magnetization fields, 49–50 results, 51–54 root-mean-square value, 53, 54 setup, 51 signal, 50 testing device, 50 theoretical analyses, 50 Magnetomotive force, 53 Mainstream acoustic emission system architecture, 58–60 MATLAB, 145 Microseismic activity, 88 Microseismic monitoring system events calculation, 412 mines, 410 multistep source localization method, 400–402 passive wave tomography, 429 Microvoid coalescence, 192 Minimum magnitude (Mmin ), 411–412 Modal acoustic emission (MAE) technique, 88, 308–309 experimental setup event acquisition, 92–93 groups, 91–92 wave mode identification, 93–94 result and analysis corrosion severity and signal waveforms, 95 single typical AE signals comparison, 95–97 theory, 89–91

448 Multicomponent variate D matrix, 188–189 Multistep source localization method analytical and iterative localization methods, 400 calculation process, 401 classification, 400 deep rockmass, 400 Kaiyang phosphorous mine, 402–403 least square method, 400 microseismic monitoring system, 400–402 mining technology, 399–400 P-wave velocity, 400, 401 results, 402–406 rockmass instability, 403 spherical interpolation method, 400 time difference method, 401 wave velocity, 404–405

N Needle-plate model, partial discharge, 390, 393 No defects (ND), 255 Nondestructive testing devices (NDT), 65 Non-integrated system, 59

O Outer raceway defects (ORD), 250–252, 255, 256

P Parameter analysis, 120 Partial discharge (PD) characteristics, 389–390 counting and energy analysis method, 390, 393–395 detection and analysis, 390–393 discharge variation, 394 experimental design, 390–391 of needle-plate model, 390, 393 plate electrode diameter, 395 time-frequency analysis, 396 voltage change, 394 Passive wave tomography, 429 PCI-1712 Advantech acquisition card, 78 Peak frequency ( fp), 142 Piezoelectric sensor (PZT), 25, 26, 78–81 See also Fiber Bragg grating (FBG) sensor Pig monitoring, 72–73 Pipeline fault diagnosis method acoustic emission signal, 327 automatic encoder, 328–330 backpropagation neural network, 327–328

Index classification deep learning neural network depth learning neural network, 330–331 feature variable selection, 332 model classification performance test, 331 pipeline status coding, 332 sample data selection, 331 deep learning neural network, 328 experimental design, 334–336 results and analysis, 336–337 implementation steps, 333–334 Plastic yield stage, 206–207, 213 Plate wave, 88 Polytec OFV505, 5 Power over Ethernet (PoE) daisy chain technology block diagram, 60 circuit board and enclosure, 61 hub, 62 multi-cables, 61 structure health monitoring applications, 63 Pre-loaded elastic stage, 206, 211–212 Pressure pipeline small leak detection methods, 305 experimental data analysis and processing, 311–313 location theory, 306–307 modal acoustic emission, 308–309 reliable and efficient method, 306 simulation experiment test, 309–310 time-frequency analysis, 308, 312 wavelet de-noising technique, 306 decomposition process, 307 reconstruction process, 307–308 threshold value process, 307 Pressure sensitivity, 6–7 Pressure vessel test, 347 Principal component analysis (PCA), 197, 200 Probabilistic entropy, 188–190 Product function (PF), 104 P-wave velocity, 400, 401

Q Q345 carbon steel, 260, 267 Q–Q plot analysis, 96 Q235 steel, 184

R Ray tracing method, 430 analytical localization method, 430 iterative localization method, 430

Index passive wave tomography, 429 shortest path method (see Shortest path method) simulation calculation ideal paths, 437 improved path, 439 minimal travel time, 437 simple plate model, 435–436 traditional path, 437–438 Snell’s law disturb path nodes, 434 P2 and P3, 435 phase velocities ratio, 433 rays pass through nodes, 434–435 x-axis direction, 433 y-axis direction, 433 travel time calculate, 439–440 wave tomography technology, 430 wave velocity tomography, 439 Reciprocating compressor valve leakage analysis, 355–356 acoustic emission signal relationship, 356 analytical valve model, 356 data preparation, 370–371 experimental setup, 358–359, 368–370 faulty machinery components, 366 flow condition, 356 Fourier transform, 366 mass flow rate, 361–362 results, 360–362 compressor speed, 372 crest factor value, 371–373 low characteristic frequency segments, 371 signal-to-noise ratio, 371, 374 single leak signal, 374 suction valve closes, 375–376 valve plate deteriorates, 374–375 valve flow rate, 356–357 valve opening and closing impact, 356 wavelet packet transform, 366–368 Reinforced concrete (RC) bridge decks, 120, 240 damage verification, 232 elastic wave, 228 internal damages, 228 in Japan, 228 load-carrying capacity and durability, 228 measurement, 229–231 secondary AE activity, 228 steel plates, 228 tomography, 229 measurement, 235 wave velocity distribution, 231 wheel loading program, 232–236 Relaxation ratio, 120

449 Rescaled range analysis (R/S analysis) method, 412 Rocks, 142 Roller element defects (RD), 250–252, 255, 256 Root-mean-square (RMS) value, 53, 54, 357, 360–361 Rotating machinery, in discrete hidden Markov chain model, 379 RS-54A AE sensor, 197

S SAMOS AEwin data acquisition system, 170 Scanning electron microscope (SEM), 170, 190–192 Scanning laser vibrometer (LVM), 10 results from, 16–18 setup, 14–15 Secondary acoustic emission activity, 228 Seismic b-value, in concrete slabs, 241 Seismic sequence Benioff strain, 413 cell evaluation method, 410 coseismic and aseismic deformation, 410 Gutenberg-Richter magnitude-frequency relationship, 411–412 Hurst exponent, 412–413 microseismic monitoring system, 410 research methods, 410–411 results and discussions, 414–417 Sensor calibration system, 4–5 ASTM E-1781-2013, standard practice, 10–11 ASTM E-1106-2017, standard test method, 10 burst signal, 4 ISO and ASTM Standards, 3 ISO 12713:1998 nondestructive testing, 11 ISO 12714:1999 nondestructive testing, 11 ISO TR13115-2011 nondestructive testing, 11 pressure sensitivity, 6–7 primary standard, 3 pulse source with normalized energy comparison, 5 R3α with different excitation source, 6 units of verification and loading effect, 22–23 Sensor under testing (SUT), 10 receiving sensitivity, 19–22 transmitting sensitivity, 14–18 Servo-hydraulic testing machine, 169 Shannon’s entropy, 168–169 Shear failure, 142 Shortest path method, 430–431 Dijkstra’s grid model, 431–432

450 Shortest path method (cont.) in graph theory, 431 velocity comparison, 432–433 See also Ray tracing method Short-time Fourier transform (STFT), 392 Shutoff and control valves (SCV) tightness test, 67–69 si255 demodulator, 78 Signal-to-noise ratio, 77 Similar evaluation method acoustic source shape discrimination, 282 definition, 280–281 detection parameters, 281 event rate, 281, 282 Sliding average filtering, 77 Small corrosion surface (SCS), 91 Snell’s law defined, 433 to disturb path nodes, 434 P2 and P3, 435 phase velocities ratio, 433 rays pass through nodes, 434–435 x-axis direction, 433 y-axis direction, 433 SOFTMAX, 330 Sound power, 356–357 Sound velocity test, in U-shaped track, 262–263 Source location technology, 347–349 Artificial Intelligence technique, 132, 134–137 in corroded concrete beam specimen, 136–138 deep learning, 132, 133 formula, 132 methodology, 132–133 numerical test, 134–135 sensors with arrival time, 132–133 2D velocity distribution model, 134–135 velocity anisotropy, 131, 134 Spacecraft gas leak detection, 421–422 helium mass spectrometer leak detection, 422 Sparse automatic encoder, 328–330 Spectrum analysis, 120 Split-Hopkinson pressure bar (SHPB) system, 143, 144 SR40M, 309 SR150 resonant sensor, 342 Stainless steel package FBG sensor, 40–41 Static tensile test data, 185–188 Steel bifurcated pipe, in hydropower station experimental study amplitude-distance attenuation test, 323–324

Index amplitude modification curve, 324–325 theoretical analysis and mathematical model amplitude, 321–322 attenuation, 321 coefficient of determination, 322 geometric diffusion attenuation, 322–323 nonlinear fitting model, 323 waveform analysis asymmetric Y type, 318 hydraulic test, 319 main pipe and branch pipe measure, 318–319 propagation distance, 320–322 welds and obstacles, 320 Stress wave emission, 75, 88, 102, 344–345 Structure health monitoring (SHM), 57–58 applications, 63 Subcritical crack propagation, 345 Suction valve opening (SVO)/suction valve closing (SVC), 360, 361 Support vector machine (SVM), 327, 328 System on chip (SoC) technology, 60

T Tank bottom in-service acoustic emission testing active defects, 271 grade and testing results, 271–272 data review, 275, 276 found defect characteristics, 274–275 lack of tank information, 274 properties, 275, 276 in-service inspection, 270 oil tanks, 270–271 steps, 271 verification, 273–274 TC4 titanium alloy distributive events vs. location, 179, 180 electron beam free-form fabrication, 177–178 location characteristics, 179–180 parameter distribution, 179 system, 178 tensile tests, 178–179 time vs. load, 179, 180 waveform and frequency spectrum, 180–181 Tensile failure, 142 Tensile tests alloy structural steel, 211–213 fatigue damage accumulation, 186–188 TC4 titanium alloy, 178–179 TensorFlow, 133

Index Terahertz time-domain spectroscopy (THz-TDS), 156 Three-dimensional (3D) woven composites applications, 195 damage modes, 196 digital image correlation, 196 displacement full-field distributions, 202–203 horizontal displacement distributions, 203 integral reinforcing fiber network, 195 materials and tensile tests, 197 multivariable analysis, 199–202 nondestructive technique, 196 reliable method, 196 tensile load and acoustic emission relative energy vs. time, 198–199 vertical displacement distributions, 202 Tightness test, of shutoff and control valves, 67–69 Time difference (TD) method, 401 ‘Time domain–frequency domain’ approach, 103 Tomography concrete slabs, 240 elastic wave, 240 passive wave, 429 reinforced concrete bridge decks, 229 measurement, 235 wave velocity distribution, 231 wave velocity, 439 Top-dead-center (TDC), 356, 358 Traditional melting welding method, friction stir welding method vs. Traditional method, of ray tracing, 430 Trajectory of damage states (TDS) curves, 189–190 Transmitting sensitivity, 14–18 2D velocity distribution model, source location, 134–135

U Ultimate tensile stress (UTS), 185 Uniaxial compression tests, of granite, 145–146 UNISCOPE concrete strength forecasting, 71–72 integral thickness measurement, 69–71 pipeline pig movement monitoring, 72–73 portable multifunction device, 66 shutoff and control valves tightness test, 67–69 solvable problems, 65–66 standard testing, 66–67 U-shaped track attenuation characteristics, 261–262

451 location analysis, 265–267 parameter characteristics, 263–264 sound velocity test, 262–263 waveform and spectrum characteristics, 264–265

V Valve leakage rate, see Reciprocating compressor valve leakage analysis Variable frequency drive (VFD), 358, 368 Velocity anisotropy, 131, 134 Velocity grid model, 439 Vertical storage tanks atmospheric pressure, 279 bottom (see Tank bottom in-service acoustic emission testing) in China, 280 qualitative safety evaluation, 280 similar evaluation method acoustic source shape discrimination, 282 definition, 280–281 detection parameters, 281 event rate, 281, 282 template cases and analysis analysis and results, 288 bottom layout, 287 corrosion perforation, 282–284 cut plate, 287 local corrosion, 284–288 VS150-RIC, 263, 264

W Waveform analysis, 120 in impact-loading tests, 147–151 Wavelet de-noising technique, 306 decomposition process, 307 reconstruction process, 307–308 threshold value process, 307 Wavelet packet eigenvector extraction, in discrete hidden Markov chain model, 381–382 Wavelet packet transform (WPT), 366–368 Wavelet threshold method, 77 Wavelet transform analysis, low-speed heavy-duty bearing, 250, 255–256 Wave propagation, 88 Wave tomography technology, 430 Wave velocity tomography, 439 Wheel loading program, by fatigue damage evaluation, 232–236