Physics-Informed Cross-Domain Fault Diagnosis for Rolling Bearings under Robustness and Interpretability: a Novel Feature Adaptation Framework
Fault diagnosis of rolling bearings is crucial for operational safety. However, the scarcity of labeled data and significant domain shifts are two key challenges. Existing studies neglect robustness to physical disturbances and the interpretability of diagnostic decisions. To address these issues, this paper proposes a physics-informed cross-domain fault diagnosis framework. First, based on bearing fault modulation mechanisms, multi-domain features are extracted. A physics-regularized feature selection strategy combining Sparse Group Lasso with stability selection and Random Forest is used to retain fault-relevant features. Recognizing that velocity variations are a major source of domain shifts, order analysis is introduced as a physics-prior alignment method. Deep CORAL and pseudo-label self-training are combined for unsupervised knowledge transfer. Finally, multi-level interpretability analysis is embedded to quantify domain shift, track adaptation geometry and explain final decisions. Two case studies demonstrate its robustness, trustworthiness and engineering applicability.