Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 319-326.doi: 10.11896/jsjkx.210700034

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Acoustic Emission Signal Recognition Based on Long Short Time Memory Neural Network

ZHOU Jun1,2, YIN Yue2, XIA Bin3   

  1. 1 Oil and Gas Postdoctoral Research Mobile Station,Army Service College,Chongqing 401331,China
    2 Chongqing Big Data Technology Application Research Institute of China Science and Technology,Chongqing 401331,China
    3 Chengyi University College,Jimei University,Xiamen,Fujian 361021,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:ZHOU Jun,born in 1981,postdoctoral,professor level senior engineer,is a member of China Computer Federation.His main research interests include signal processing and artificial intelligence.
  • Supported by:
    Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K201904401,KJZD-K202004401),Research on the Practice of Improving the Efficiency of Funding Work for Poor Students in Higher Vocational Colleges from the Perspective of Artificial Intelligence(2020-GX-414),2021 Chongqing Education Commission Humanities and Social Science Research Student Funding Research(21skxszz01) and Chongqing Vocational College of Commerce Artificial Intelligence Technology Application Collaborative Innovation Center Funded Project.

PDF (PC)

1118

Abstract: Acoustic emission testing does not need to enter the tested object for testing,and compared with traditional nondestructive testing technology,acoustic emission testing has unique advantages such as real-time,integrity and high sensitivity.Parameter analysis and wavelet analysis methods was used in the acoustic emission signal feature extraction,which lack of theoretical guidance and certain subjectivity.The BP neural network training easy to fall into local extremum when used in the acoustic emission signal recognition,Long Short-Term Memory neural network can learn step by step input sequence data and adaptive feature extraction,avoids artificial selection and extraction of the feature.Z-score standardization was used in the acoustic emission signal,LSTM neural network uses single hidden layer structure,compare the different correct recognition rate on the test set in different situations of learning algorithm,the number of hidden layer neurons,the output neurons dropout rate,optimal model of acoustic emission signal recognition based on LSTM structure was found out.Comparing the acoustic emission signal recognition accuracy and with the BP neural network,experimental results show that the recognition rate of LSTM neural network is 76.51% under the setting of Adam algorithm,250 hidden layer neurons and 0.5 dropout rate,Compared with the highest recognition rate of 53.9% of BP neural network,the proposed algorithm has advantages.

Key words: Acoustic emission signal, BP neural network, LSTM neural network, Recognition

CLC Number: 

  • TP183
[1]FAN Z,HU M,ZHANG K,et al.A review of research on acoustic emission on-line monitoring of oil and gas pipe corrosion in acidic environments[J].Surface Technology,2019,48(4):245-252.
[2]CHI D Z,QI C C.Review of domestic research on acoustic nondestructive testing of welding defects[J].Precision Forming Engineering,2018,10(1):74-81.
[3]LIU G H.Research on key technologies of acoustic emission[D].Hangzhou:Zhejiang University,2008.
[4]SHIWA M.Recognition of rub-impact acoustic emission signal based on fuzzy entropy[J].Journal of Mechanical Engineering,2010,46(3):71-75.
[5]LIM J,MICRO K T.Cracking in stainless steel pipe detection by using acoustic emission and crest factor technique[C]//Instrumentation and Measurement Technology Conference.Warsaw,Poland,2007:1-3.
[6]MA Y H,LIU K,YANG D Z,et al.Based on typical BP network tensile acoustic emission signal feature recognition of metal specimens [J].Journal of Sichuan University (Engineering Science Edition),2011,43(2):252-255.
[7]SAUSE M G R,GRIBOV A,UNWIN A R.Pattern recognition approach to identify natural clusters of acoustic emission signals[J].Pattern Recognition Letters,2012,33(1):17-23.
[8]SUZUKI H,KINJO T,TAKEMOTO M,et al.Fracture-mode determination of glass-fiber composites by various AE processing[J].Japanese Journal of Applied Physics,1997,36(5B),3281.
[9]GANG Q,ALAN B,JAVAD H,et al.The wavelet transform in the acoustic emission signal feature extraction of the rubbing fault[C]//1st International Workshop on Database Technology and Applications,DBTA 2009.Wuhan,China,2009:283-286.
[10]CUI Y,LI X L,PENG H X,et al.Interface characterization of discontinuous reinforced metal matrix composites based on acoustic emission wavelet analysis[J].Science Bulletin,2010,43(6):656-657.
[11]FENG Z G,SUN R K,GOU J J,et al.Acoustic emission source location method based on wavelet packet analysis [J].Journal of Jiangsu University,2010,31(1):109-111.
[12]ZHANG Z,WU X Q,TAN J B.In-situ monitoring of stress corrosion cracking of 304 stainless steel in high-temperature water by analyzing acoustic emission waveform[J].Corrosion Science,2018,146:90-98.
[13]SACHES W,GRABEC I.Intelligent processing of acoustic emission signals[J].Materials Evaluation,1992,50(7):826-854.
[14]INGRAHAM M D,ISSEN K A,HOLCOMB D J.Use of acoustic emissions to investigate localization in high-porosity sandstone subjected to true triaxial stresses[J].Acta Geotechnica,1998,8(6).
[15]KOSEL T,GRABEC I,MUŽI P.Location of acoustic emission sources generated by air flow[J].Ultrasonics,2000,38(1-8):824-826.
[16]ZHAO Y X,XU Y G,GAO L X,et al.Rolling bearing acoustic emission fault pattern recognition technology based on harmonic wavelet packet and BP neural network[J].Vibration and Shock,2010,29(10):163-166.
[17]MAO H Y,CHENG J G,HUANG Z F.Extraction of characteristic parameters of acoustic emission signal of metal crack based on BP neural network [J].Mechanical Design,2010,27(2):84-86.
[18]ZHOU J,WANG Q,YI M,et al.Acoustic emission signal recognition based on wavelet transform and RBF network[J].Journal of Petrochemical College,2015,28(3):80-85.
[19]GUAN S,PANG H Y,SONG W J,et al.Tool wear status recognition based on MF-DFA features and LS-SVM algorithm[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(14):61-68.
[20]SI L,BI G H,WEI Y G,et al.Acoustic emission signal detection and recognition method based on RQA and SVM[J].Vibration and Shock,2016,35(2):97-103,123.
[21]WANG X Y,JIANG Z W,CHEN H Q,et al.Research on pipeline leak diagnosis method combining SVM and DS[J].Computer Engineering and Applications,2016,52(5):262-265,270.
[22]HU Y,LUO D Y,HUA K,et al.A review and discussion on deep learning [J].Journal of Intelligent Systems.2019,14(1):1-19.
[23]ELDAN R,SHAMIR O.The power of depth for feedforward neural networks[C]//JMLR:Workshop and Conference Proceedings.2016:1-34.
[24]HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[25]HOCHREITER S,SCHMIDHUBER J.Long short-term memory [J].Neural computation,1997,9(8):1735-1780.
[26]SILVER D,HUANG A,MADDISON C J,et al.Mastering the game of Go with deep neural networks and tree search[J].Nature,2016,529(7587):484-489.
[27]ZHU Y,GAO X,ZHANG W,et al.A bi-directional LSTM-CNN model with attention for aspect-level text classification [J].Future Internet,2018,10(12):116.
[28]YIN Z K,LIAO W H,WANG R J,et al.Simulation and prediction of rainfall and runoff based on long and short-term memory neural network (LSTM)[J].South-to-North Water Diversion and Water Conservancy Science and Technology,2019,17(6):1-9,27.
[29]FISCHER T,KRAUSS C.Deep learning with long short-term memory networks for financial market predictions [J].European Journal of Operational Research,2018,270(2):654-669.
[30]ZENG W G,LI S H,LI Y,et al.Evaluation of uniform corrosion defects of oil,gas and water gathering pipelines based on radial basis function neural network prediction model[J].Corrosion and Protection,2020,41(10):50-56.
[31]ZHANG J K,ZHAO J,ZHANG R,et al.A review of image instance segmentation methods based on deep learning [J].Small Microcomputer System,2021,42(1):161-171.
[32]YAMASHITA T,ZHANG M.Graphical Deep Learning [M].Beijing:People's Posts and Telecommunications Press,2018:46-47.
[33]ZHOU J.Research on Acoustic Emission Signal Processing Algorithm Based on Machine Learning [M].Beijing:Publishing House of Electronics Industry,2020:31-32.
[34]CHOLET F.Python Deep Learning [M].Beijing:People's Posts and Telecommunications Press,2018:59-60.
[35]KINGMA D,BA J.Adam:A Method for Stochastic Optimization[J].Computer Science,2014.
[36]ZHU J Y,WANG R B,HUANG X X,et al.Multi-level metaphor recognition method based on Bi-LSTM [J].Journal of Dalian University of Technology,2020,60(2):209-215.
[37]JIANG Z L.Introduction to Artificial Neural Networks [M].Beijing:Higher Education Press,2002:32-33.
[1] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[2] YANG Bing-xin, GUO Yan-rong, HAO Shi-jie, Hong Ri-chang. Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition [J]. Computer Science, 2022, 49(7): 57-63.
[3] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[4] FEI Xing-rui, XIE Yi. Click Streams Recognition for Web Users Based on HMM-NN [J]. Computer Science, 2022, 49(7): 340-349.
[5] LIU Bao-bao, YANG Jing-jing, TAO Lu, WANG He-ying. Study on Prediction of Educational Statistical Data Based on DE-LSTM Model [J]. Computer Science, 2022, 49(6A): 261-266.
[6] HAO Qiang, LI Jie, ZHANG Man, WANG Lu. Spatial Non-cooperative Target Components Recognition Algorithm Based on Improved YOLOv3 [J]. Computer Science, 2022, 49(6A): 358-362.
[7] GAO Rong-hua, BAI Qiang, WANG Rong, WU Hua-rui, SUN Xiang. Multi-tree Network Multi-crop Early Disease Recognition Method Based on Improved Attention Mechanism [J]. Computer Science, 2022, 49(6A): 363-369.
[8] ZHANG Jia-hao, LIU Feng, QI Jia-yin. Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer [J]. Computer Science, 2022, 49(6A): 370-377.
[9] XIAO Zhi-hong, HAN Ye-tong, ZOU Yong-pan. Study on Activity Recognition Based on Multi-source Data and Logical Reasoning [J]. Computer Science, 2022, 49(6A): 397-406.
[10] HUANG Pu, DU Xu-ran, SHEN Yang-yang, YANG Zhang-jing. Face Recognition Based on Locality Regularized Double Linear Reconstruction Representation [J]. Computer Science, 2022, 49(6A): 407-411.
[11] WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
[12] HUANG Pu, SHEN Yang-yang, DU Xu-ran, YANG Zhang-jing. Face Recognition Based on Locality Constrained Feature Line Representation [J]. Computer Science, 2022, 49(6A): 429-433.
[13] LIU Wei-ye, LU Hui-min, LI Yu-peng, MA Ning. Survey on Finger Vein Recognition Research [J]. Computer Science, 2022, 49(6A): 1-11.
[14] DU Xiao-ming, YUAN Qing-bo, YANG Fan, YAO Yi, JIANG Xiang. Construction of Named Entity Recognition Corpus in Field of Military Command and Control Support [J]. Computer Science, 2022, 49(6A): 133-139.
[15] GUO Xing-chen, YU Yi-biao. Robust Speaker Verification with Spoofing Attack Detection [J]. Computer Science, 2022, 49(6A): 531-536.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.