Bearing fault diagnosis based on wavelet sparse convolutional network and acoustic emission compression signals

Jinyi Tai; Chang Liu; Xing Wu; Jianwei Yang; Jinyi Tai; Chang Liu; Xing Wu; Jianwei Yang

doi:10.3934/mbe.2022377

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 8: 8057-8080. doi: 10.3934/mbe.2022377

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Research article

Bearing fault diagnosis based on wavelet sparse convolutional network and acoustic emission compression signals

1.
Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Prov-ince, Kunming University of Science & Technology, Kunming 650500, China
2.
Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China
3.
Yunnan Vocational College of Mechanical and Flectrical Technology, Kunming 650203, China

Academic Editor: Yang Kuang

Received: 13 April 2022 Revised: 18 May 2022 Accepted: 23 May 2022 Published: 02 June 2022

A bearing is an important and easily damaged component of mechanical equipment. For early fault diagnosis of ball bearings, acoustic emission signals are more sensitive and less affected by mechanical background noise. To cope with the large amount of data brought by the high sampling frequency and high sampling points of acoustic emission signals, a compressed sensing processing framework is introduced to research data compression and feature extraction, and a wavelet sparse convolutional network is proposed for resolved diagnosis and evaluation. The main research objective of this paper is to maximize the compression rate of the signal under the constraint of ensuring the reconstruction error of the acoustic emission signal, which can reduce the data volume of the acoustic emission signal and reduce the pressure of data analysis for subsequent fault diagnosis. At the same time, a wide convolution kernel based on a continuous wavelet is introduced when designing the neural network, and the energy information of different frequency bands of the signal is extracted by the wavelet convolution kernel to characterize the fault characteristics of the equipment. The energy pooling layer is designed to enhance the deep mining ability of compressed features, and the regularized loss function is introduced to improve the diagnostic accuracy and robustness through feature sparseness. The experimental results show that the method can effectively extract the fault characteristics of the bearing acoustic emission signal, improve the analysis efficiency and accurately classify the bearing faults.
- acoustic emission signal,
- compressed sensing,
- wavelet transform,
- convolution kernel,
- energy pooling layer,
- sparse feature
Citation: Jinyi Tai, Chang Liu, Xing Wu, Jianwei Yang. Bearing fault diagnosis based on wavelet sparse convolutional network and acoustic emission compression signals[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 8057-8080. doi: 10.3934/mbe.2022377

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Abstract

A bearing is an important and easily damaged component of mechanical equipment. For early fault diagnosis of ball bearings, acoustic emission signals are more sensitive and less affected by mechanical background noise. To cope with the large amount of data brought by the high sampling frequency and high sampling points of acoustic emission signals, a compressed sensing processing framework is introduced to research data compression and feature extraction, and a wavelet sparse convolutional network is proposed for resolved diagnosis and evaluation. The main research objective of this paper is to maximize the compression rate of the signal under the constraint of ensuring the reconstruction error of the acoustic emission signal, which can reduce the data volume of the acoustic emission signal and reduce the pressure of data analysis for subsequent fault diagnosis. At the same time, a wide convolution kernel based on a continuous wavelet is introduced when designing the neural network, and the energy information of different frequency bands of the signal is extracted by the wavelet convolution kernel to characterize the fault characteristics of the equipment. The energy pooling layer is designed to enhance the deep mining ability of compressed features, and the regularized loss function is introduced to improve the diagnostic accuracy and robustness through feature sparseness. The experimental results show that the method can effectively extract the fault characteristics of the bearing acoustic emission signal, improve the analysis efficiency and accurately classify the bearing faults.

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