Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising. (September 2022)

• Record Type: Journal Article
• Authors: Wang, Huan; Liu, Zhiliang; Peng, Dandan; Cheng, Zhe
• Is Part Of: ISA transactions. Volume 128(2022)Part B
• Page Start: 470
• Page End: 484
• Publication Date: 2022-09
• Subjects: Wheelset bearing -- Fault diagnosis -- Vibration signal processing -- Convolutional neural network; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.11.028
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Mechanical system usually operates in harsh environments, and the monitored vibration signal faces substantial noise interference, which brings great challenges to the robust fault diagnosis. This paper proposes a novel attention-guided joint learning convolutional neural network (JL-CNN) for mechanical equipment condition monitoring. Fault diagnosis task (FD-Task) and signal denoising task (SD-Task) are integrated into an end-to-end CNN architecture, achieving good noise robustness through dual-task joint learning. JL-CNN mainly includes a joint feature encoding network and two attention-based encoder networks. This architecture allows FD-Task and SD-Task can achieve deep cooperation and mutual learning. The JL-CNN is evaluated on the wheelset bearing dataset and motor bearing dataset, which shows that JL-CNN has excellent fault diagnosis ability and signal denoising ability, and it has good performance under strong noise and unknown noise. Highlights: The feasibility of vibration signal denoising based on deep learning has been studied. An excellent end-to-end CNN-based vibration signal denoising method is proposed. A new solution to improve the noise robustness of the fault diagnosis model is proposed. A new network framework based on joint learning is designed, which performs well under unknown noise.