Wavelet residual shrinkage network: a robust approach for bearing fault diagnosis in noisy environments

In industrial equipment monitoring, how to accurately assess the fault severity of rolling bearings remains a challenging problem. Existing research mainly focuses on fault classification under Gaussian noise, but there are few studies on fault severity classification in complex noise environments. Although the Deep Residual Shrinkage Network (DRSN) can enhance feature selection through the Shrinkage mechanism, it is difficult for traditional convolution to ensure the accuracy and noise robustness at the same time when dealing with signals polluted by complex noise. In order to solve this problem, this paper proposes a Wavelet-based Residual Shrinkage Network (WRSN), which incorporates a wavelet decomposition convolution (WDConv) module. This module can decompose the STFT spectrogram into low-frequency smooth components and high-frequency detailed components, so as to extract fault features more accurately than traditional convolution. The experimental results on the HUSTbearing dataset show that WRSN successfully strikes a balance between diagnostic accuracy, noise resilience and computational efficiency.