Physics-Consistent Sparse Learning for Cross-Domain Rotor Fault Diagnosis

Robust rotor fault diagnosis is challenging when labeled fault data are limited and operating conditions differ between synthetic and real environments. Existing domain-adaptation methods can reduce distributional discrepancies, but they may retain redundant features and provide limited physical interpretability. This study proposes a physics-guided sparse-entropy domain adaptation framework for robust rotor fault diagnosis under such distribution shifts. Bearing eccentricity and damping are identified from experimental measurements and used to construct a physics-consistent rotor model. Synthetic fault data are then generated by incorporating representative defects, including unbalance and misalignment, while unlabeled signals, acquired from a laboratory rotor test rig, are used as the target domain. A ResNet-based feature extractor is trained using a hybrid strategy that combines labeled synthetic data with unlabeled experimental data. Maximum Mean Discrepancy and a Domain-Adversarial Neural Network are employed to reduce the source–target distribution discrepancy. In addition, sparsity and entropy regularization are incorporated into the objective function to suppress redundant representations and enhance fault-discriminative features. A Monte Carlo simulation-based optimization strategy is used to balance classification, domain alignment, and sparse–entropy constraints. Experimental results demonstrate an average target-domain accuracy of 94.04%, with a transfer gap of only 5.16%. The proposed framework also achieves higher diagnostic accuracy than DANN, Correlation Alignment, and MMD-based approaches. These results demonstrate its potential for data-efficient and physically interpretable rotor condition monitoring under varying operating conditions.