A Physics-informed Consistency Learning Framework for Robust Condition Monitoring of AUVs in Extreme Environments

Ensuring the survivability of Autonomous Underwater Vehicles (AUVs) in unstructured, disturbance-prone environments leads to a significant challenge due to sensor degradation and physical uncertainties. Conventional deep learning models often suffers from rapid decline in performance and limited interpretability under such conditions. To address the sim-to-real gap, this study proposes the Physics-Informed Residual Transformer (PIR-Transformer), which integrates two key components: 1) a Physics-Based Residual Mechanism that enforces consistency among sensor and actuator signals, and 2) an Environment-Adaptive Consistency Learning strategy that introduces simulated &quotMicroshift&quot noise during training. By minimizing divergence between clean and perturbed predictions, the model learns robustness to environmental variations. Evaluation on the Haizhe dataset shows that the proposed method achieves a macro F1-score of 0.7316 under extreme-noise (N3), outperforming the best baseline by 0.1759. Unlike conventional models, which exhibit severe degradation, the PIR-Transformer maintains stable performance, demonstrating that combining physics-based constraints with adaptive learning effectively enhances robustness in real-world conditions.