Multiscale bearing fault diagnosis based on wavelet weight initialization and channel attention

To address the issue of low diagnostic accuracy of convolutional networks for bearing faults under noisy conditions, a Convolutional Neural Network model (WMCNN) incorporating wavelet weight initialization and multi-scale attention is proposed. Firstly, considering the crucial role of the first convolutional layer in model robustness, a wavelet weight initialization layer is introduced to selectively filter features, imparting them with multi-scale characteristics. Subsequently, for extracting features at different scales, channel attention is employed to adaptively select channels containing fault features, thereby enhancing the model's noise resistance and suppressing noise interference. Additionally, adaptive one-dimensional convolution is utilized to adjust channel weights of features at different scales, facilitating adaptive fusion of multi-scale features. Finally, feature classification is performed through fully connected layers. Experimental results on bearing datasets demonstrate that, under varying signal-to-noise ratios and noise interference, WMCNN exhibits superior bearing fault diagnostic capability compared to other methods.