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

计算机科学 降噪 人工智能 深度学习 卷积神经网络 模式识别(心理学)
作者
Yi Qin,Rui Yang,Biao He,Dingliang Chen,Yongfang Mao
出处
期刊:Isa Transactions [Elsevier BV]
卷期号:147: 55-70 被引量:8
标识
DOI:10.1016/j.isatra.2024.01.027
摘要

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.
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