Acoustic Emission Recognition Based on a Three-Streams Neural Network with Attention  

作　　者：Kang Xiaofeng Hu Kun Ran Li 

机构地区：[1]College of Information and Engineering,Xuzhou University of Technology,Xuzhou,Jiangsu,221000,China [2]College of Electrical and Power Engineering,China University of Mining and Technology,Xuzhou,Jiangsu,221116,China [3]Department of Electrical,Electronic and Computer Engineering,University of Western Australia,Perth,Australia

出　　处：《Computer Systems Science & Engineering》2023年第9期2963-2974,共12页计算机系统科学与工程（英文）

摘　　要：Acoustic emission(AE)is a nondestructive real-time monitoring technology,which has been proven to be a valid way of monitoring dynamic damage to materials.The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning.Considering that the huge success of deep learning technologies,where the Recurrent Neural Network(RNN)has been widely applied to sequential classification tasks and Convolutional Neural Network(CNN)has been widely applied to image recognition tasks.A novel three-streams neural network(TSANN)model is proposed in this paper to deal with fault detection tasks.Based on residual connection and attention mechanism,each stream of the model is able to learn the most informative representation from Mel Frequency Cepstrum Coefficient(MFCC),Tempogram,and short-time Fourier transform(STFT)spectral respectively.Experimental results show that,in comparison with traditional classification methods and single-stream CNN networks,TSANN achieves the best overall performance and the classification error rate is reduced by up to 50%,which demonstrates the availability of the model proposed.

关 键 词：Convolutional neural network attention mechanism acoustic emission fault detection 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]