Transferability-Guided Residual Attention Domain Adaptation for Unsupervised Cross-Condition Aero-Engine Gas-Path Fault Diagnosis
Cross-condition aero-engine gas-path fault diagnosis remains challenging because gas-path parameters exhibit heterogeneous transferability, hidden feature distributions shift substantially across operating conditions, and the class structure of the unlabeled target domain is often unstable. To address these issues, this study proposes a Transferability-Guided Residual Attention Domain Adaptation Network (TG-RADAN) for unsupervised cross-condition aero-engine gas-path fault diagnosis. First, a Transferability Index (TI) is constructed by jointly considering source-domain fault discriminability and cross-domain distribution stability. Based on the TI, a learnable feature-gating mechanism is introduced to adaptively reweight gas-path parameters and suppress operating-condition-sensitive features. Second, a residual-attention encoder with domain-specific batch normalization is developed to enhance fault-discriminative representations while mitigating hidden-layer statistical discrepancies between domains. Third, conditional adversarial domain adaptation, high-confidence pseudo-label prototype alignment, and target entropy minimization are jointly employed to improve class-conditional alignment and preserve the target-domain structure. Experiments on 12 cross-condition transfer tasks demonstrate that TG-RADAN achieves average accuracies of 94.43% and 94.41% on the two groups of transfer tasks, respectively, outperforming the conventional CDAN baseline by 11.94% and 12.91%. Ablation results show that the complete model improves upon the model without TI-guided gating by 2.69% and 3.20%, respectively. These findings indicate that TG-RADAN can effectively improve the transferability, robustness, and interpretability of unsupervised cross-condition aero-engine gas-path fault diagnosis.