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 optimization of the model. Additionally, we introduce a joint maximum mean discrepancy method to measure the distance between the source and target domains. Experimental results demonstrate that the proposed approach achieves superior diagnostic accuracy and offers reliable support for monitoring and predicting the state of rotating machinery.