Abstract
Railway bearings experience speed- and load-dependent distribution shifts that complicate fault diagnosis. This study proposes a Physics-Guided Multi-View Transformer with Block Attention Residuals (PGMV-BAR). Characteristic-order prior tokens sequentially query envelope-order and time–frequency features and remain in the fusion sequence with a separate condition token. A two-stage strategy combines masked reconstruction, contrastive consistency, prior alignment, and supervised fine-tuning. Evaluation follows a source-only cross-condition protocol. Beijing Jiaotong University–Rail Autonomous Operations (BJTU-RAO) includes nine combinations of motor frequency (20, 40, and 60 Hz) and lateral load (−10, 0, and +10 kN), with a constant vertical load of 10 kN. All 36 unordered pairs are evaluated as held-out test conditions, with the remaining seven conditions supplying source training and validation in each task. The L dataset is evaluated through six leave-one-condition-out tasks formed by 1200, 1500, and 1800 r/min at 0% and 10% rated torque, using the other five conditions as sources. Held-out-condition samples are excluded from pretraining, fine-tuning, and checkpoint selection. PGMV-BAR achieves a mean accuracy of 97.27% across the 36 BJTU-RAO tasks (96.05–99.21%) and 95.33% across the L tasks. These results support cross-condition diagnosis within the evaluated datasets.