A bearing fault diagnosis method based on Swin transformer optimization

This article proposes an optimization method for bearing fault diagnosis based on Swin Transformer. Extract features from the bearing dataset through continuous wavelet transform; Then, the obtained features are input into the Swin Transformer model optimized by the HHO optimization algorithm for learning and classification. Output classification results, accuracy, and loss values. By comparing models such as CNN, LSTM, RESNET, GRU. The Swin Transformer model has an accuracy index of 21.01%, respectively; 8.19%; 5.63%; 2.71% increase. The Swin Transformer optimized by the HHO algorithm achieved an accuracy of 99.97%. Greatly improves the accuracy, robustness, and generalization of bearing fault diagnosis.