Angular Margin Consistency-Based Physical Transformer for Planetary Gearboxes Cross-Condition Fault Diagnosis Under Noisy Environments

Cross-condition fault diagnosis of planetary gearboxes based on domain generalization (DG) has gained significant attention recently. However, most existing methods mainly rely on data-driven approaches and neglect the physical characteristics of planetary gearboxes. Moreover, signals collected in real industrial environments are often corrupted by noise, which reduces the generalization ability of diagnostic models. To address these challenges, this article proposes an angular margin consistency-based physical Transformer (AMC-PT). First, the model incorporates a physical encoding layer that enhances fault-related modal characteristics and suppresses noise, thereby improving cross-condition diagnosis under noisy environments. Second, instead of extracting only domain-invariant features, the model employs an angular margin consistency loss to capture stable relationships between each fault category and the healthy category across conditions. Lastly, a two-step inference strategy is designed to refine predictions under target conditions. Experiments on 18 cross-domain fault diagnosis tasks demonstrate that the proposed AMC-PT significantly improves performance in noisy environments and exhibits strong generalization.