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

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. • 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. [ABSTRACT FROM AUTHOR]