Please use this identifier to cite or link to this item: https://doi.org/10.32604/CMC.2020.09801
Title: Acoustic emission recognition based on a two-streams convolutional neural network
Authors: Yang, W.
Liu, W.
Liu, J.
Zhang, M. 
Keywords: Acoustic emission
Convolutional neural network
Fault detection
Issue Date: 2020
Publisher: Tech Science Press
Citation: Yang, W., Liu, W., Liu, J., Zhang, M. (2020). Acoustic emission recognition based on a two-streams convolutional neural network. Computers, Materials and Continua 64 (1) : 515-525. ScholarBank@NUS Repository. https://doi.org/10.32604/CMC.2020.09801
Abstract: The Convolutional Neural Network (CNN) is a widely used deep neural network. Compared with the shallow neural network, the CNN network has better performance and faster computing in some image recognition tasks. It can effectively avoid the problem that network training falls into local extremes. At present, CNN has been applied in many different fields, including fault diagnosis, and it has improved the level and efficiency of fault diagnosis. In this paper, a two-streams convolutional neural network (TCNN) model is proposed. Based on the short-time Fourier transform (STFT) spectral and Mel Frequency Cepstrum Coefficient (MFCC) input characteristics of two-streams acoustic emission (AE) signals, an AE signal processing and classification system is constructed and compared with the traditional recognition methods of AE signals and traditional CNN networks. The experimental results illustrate the effectiveness of the proposed model. Compared with single-stream convolutional neural network and a simple Long Short-Term Memory (LSTM) network, the performance of TCNN which combines spatial and temporal features is greatly improved, and the accuracy rate can reach 100% on the current database, which is 12% higher than that of single-stream neural network. © 2020 Tech Science Press. All rights reserved.
Source Title: Computers, Materials and Continua
URI: https://scholarbank.nus.edu.sg/handle/10635/197351
ISSN: 15462218
DOI: 10.32604/CMC.2020.09801
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