Bearing fault diagnosis method using CNN with denoising structure under strong noise background

Abstract In the monitoring of rotating machinery area, intelligent fault diagnosis based on signal analysis has been widely applied. However, due to modulation of the hardware transmission path and interference from environmental noise, the quality of collected vibration signals is prone to degradation. Convolutional neural networks (CNNs) are currently the most widely used models for fault diagnosis. However, their lack of dedicated denoising structures makes them less robust against noise. Therefore, this paper proposes an end-to-end denoising CNN fault diagnosis model. Firstly, a discrete-wavelet attention layer (DAL) and convolutional layers are alternately employed to extract signal features in the wavelet domain. Secondly, according the periodic self-similarity of vibration signals, the Gramian noise reduction (GNR) method is utilized to enhance fault features in the signal. Subsequently, GNR and DAL are integrated into the model to simultaneously extract features from the original signal and the vibration signal enhanced by GNR, thereby enhancing the model fault diagnosis performance in noisy environments. Finally, various levels of noise are added to Case Western Reserve University and Dalian University of Technology data, and compared with other advanced methods, to verify the effectiveness and universality of the proposed method.