Self-supervised learning for train bearing fault diagnosis based on time–frequency dual domain prediction

Train traction motor bearings (TTMBs) are critical for ensuring stable and safe train operation. However, most intelligent fault diagnosis methods face significant challenges due to the complexity and variability of TTMB fault data, as well as the high cost of obtaining labeled data. Accordingly, this paper introduces a self-supervised learning model that leverages time–frequency dual domain prediction (TFDDP). First, a data augmentation module is designed to generate diverse training data, thereby enhancing the model’s robustness and generalization ability under different conditions. Secondly, an encoder combining multiple attention mechanisms and residual networks is proposed to extract critical…

• NN
  National Natural Science Foundation of China

  Award: U22A2053