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.

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
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