Innovative bearing fault diagnosis using SST algorithm for time-frequency images. Accurately transform signals into efficient time-frequency representations. Leverage deep learning for precise diagnosis. Open-source for collaboration, advancing bearing fault diagnosis.
Time-frequency images corresponding to the three transformation methods
The T-SNE feature maps corresponding to the three transformation methods
Fourteen fold accuracy of three transformation methods under DeepViT model