Rolling Bearing Fault Diagnosis Based on Cross-Attention Fusion WDCNN and BILSTM

High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is used to obtain global time series features of the signal. Cross-attention combines the WDCNN layer and the BILSTM layer so that the model can recognize more comprehensive feature information of the signal. Meanwhile, to improve the accuracy, Variable Modal Decomposition (VMD) is used to decompose the signals and filter and reconstruct the signals using envelope entropy and kurtosis, which enables the pre-processing of the signals so that the data input to the neural network contains richer feature information. The feasibility of the model is tested and experimentally validated using publicly available datasets. The experimental results show that the accuracy of the model proposed in this paper is significantly improved compared to the traditional WDCNN, BILSTM, and WDCNN-BILSTM models.