Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults

Li, Sheng, Ji, JC, Xu, Yadong, Feng, Ke, Zhang, Ke, Feng, Jingchun, Beer, Michael ORCID: 0000-0002-0611-0345, Ni, Qing and Wang, Yuling (2024) Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults. Mechanical Systems and Signal Processing, 210. p. 111142.

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Official URL: http://dx.doi.org/10.1016/j.ymssp.2024.111142

Abstract

Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to entire industrial applications. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status. However, the complex working conditions of rolling bearings often make the fault-related information easily buried in noise and other interference. Therefore, it is challenging for existing approaches to extract sufficient critical features in these scenarios. To address this issue, this paper proposes a novel CNN-Transformer network, referred to as Dconformer, capable of extracting both local and global discriminative features from noisy vibration signals. The main contributions of this research include: (1) Developing a novel joint-learning strategy that simultaneously enhances the performance of signal denoising and fault diagnosis, leading to robust and accurate diagnostic results; (2) Constructing a novel CNN-transformer network with a multi-branch cross-cascaded architecture, which inherits the strengths of CNNs and transformers and demonstrates superior anti-interference capability. Extensive experimental results reveal that the proposed Dconformer outperforms five state-of-the-art approaches, particularly in strong noisy scenarios.

Item Type:	Article
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	06 Feb 2024 08:29
Last Modified:	06 Feb 2024 08:36
DOI:	10.1016/j.ymssp.2024.111142
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3178395