Multi-layer convolutional dictionary learning network for signal denoising and its application to explainable rolling bearing fault diagnosis

As a vital mechanical sub-component, the health monitoring of rolling bearings is important. Vibration signal analysis is a commonly used approach for fault diagnosis of bearings. Nevertheless, the collected vibration signals cannot avoid interference from noises which has a negative influence on fa...

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Vydáno v:ISA transactions Ročník 147; s. 55 - 70
Hlavní autoři: Qin, Yi, Yang, Rui, He, Biao, Chen, Dingliang, Mao, Yongfang
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.04.2024
Témata:
Dictionary learning
Multi-layer sparse coding
Signal denoising
Sparse representation
Unrolling
Multi-layer sparse coding
Sparse representation
Unrolling
Signal denoising
Dictionary learning
ISSN:0019-0578, 1879-2022, 1879-2022
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Shrnutí:As a vital mechanical sub-component, the health monitoring of rolling bearings is important. Vibration signal analysis is a commonly used approach for fault diagnosis of bearings. Nevertheless, the collected vibration signals cannot avoid interference from noises which has a negative influence on fault diagnosis. Thus, denoising needs to be utilized as an essential step of vibration signal processing. Traditional denoising methods need expert knowledge to select hyperparameters. And data-driven methods based on deep learning lack interpretability and a clear justification for the design of architecture in a “black-box” deep neural network. An approach to systematically design neural networks is by unrolling algorithms, such as learned iterative soft-thresholding (LISTA). In this paper, the multi-layer convolutional LISTA (ML-CLISTA) algorithm is derived by embedding a designed multi-layer sparse coder to the convolutional extension of LISTA. Then the multi-layer convolutional dictionary learning (ML-CDL) network for mechanical vibration signal denoising is proposed by unrolling ML-CLISTA. By combining ML-CDL network with a classifier, the proposed denoising method is applied to the explainable rolling bearing fault diagnosis. The experiments on two bearing datasets show the superiority of the ML-CDL network over other typical denoising methods. •The multi-layer convolutional LISTA (ML-CLISTA) algorithm is derived.•The multi-layer convolutional dictionary learning network is proposed.•The proposed method is applied to the explainable rolling bearing fault diagnosis.
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ISSN:0019-0578
1879-2022
1879-2022
DOI:10.1016/j.isatra.2024.01.027