Open Access

ARTICLE

Acoustic Emission Recognition Based on a Two-Streams Convolutional Neural Network

Weibo Yang¹, Weidong Liu^{2, *}, Jinming Liu³, Mingyang Zhang⁴

1 School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, China.
2 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221000, China.
3 School of Information Science and Engineering, Southeast University, Nanjing, 211100, China.
4 Department of Electrical and Computer Engineering, National University of Singapore, 119077, Singapore.

* Corresponding Author: Weidong Liu. Email: .

Computers, Materials & Continua 2020, 64(1), 515-525. https://doi.org/10.32604/cmc.2020.09801

Received 19 January 2020; Accepted 05 April 2020; Issue published 20 May 2020

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.

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Keywords

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