An unsupervised transfer learning bearing fault diagnosis method based on multi-channel calibrated Transformer with shiftable window

Bearings, being essential to modern industry, demand reliable cross-domain diagnostic methods. We propose a transfer learning method for bearing fault diagnosis based on a multi-channel Transformer model with shift windows. The relationship between segmented patch sequences was modeled through self-attention calculation using non-overlapping shift windows. A new partitioning strategy is employed that shifts windows and alternates between two distinct methods to create cross-window connections. To capture basic signal features while preserving positional details, several convolutional layers are introduced prior to the Transformer block. We propose a multi-channel calibration module to ensure stable…

• FR
  Fundamental Research Funds for the Central Universities

  Award: No.2572022BF07