Open Access

ARTICLE

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

Kang Xiaofeng¹, Hu Kun^2,*, Ran Li³

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

* Corresponding Author: Hu Kun. Email:

Computer Systems Science and Engineering 2023, 46(3), 2963-2974. https://doi.org/10.32604/csse.2023.025908

Received 09 December 2021; Accepted 09 February 2022; Issue published 03 April 2023

Abstract

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.

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Keywords

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