Explainable Remaining Useful Life Prediction of Air Circuit Breakers via Physics-Informed Electro-Mechanical Feature...

Accurate remaining useful life (RUL) prediction of air circuit breakers (ACBs) is crucial for condition-based maintenance. However, existing data-driven prognostic methods suffer from electromechanical feature fragmentation, cross-device domain shifts, and the inability to penalize safety-critical late predictions. This study proposes an explainable RUL prediction framework via physics-informed feature fusion. Through full-lifecycle monitoring, novel indicators, including the electromechanical coupled degradation index (EMCDI) and the contact spring over-travel consumption rate (CSOCR), are introduced to decode interactive degradation cycles. To eliminate the interferences of initial manufacturing tolerances, a phase-decoupled normalization strategy empowers a random forest (RF) model to achieve cross-device transferability in a two-device proof-of-concept experiment, requiring only 50 initial operations for target calibration. Additionally, a safety-oriented asymmetric penalty score (APS) is integrated into the evaluation framework to explicitly penalize hazardous life overestimations. Experimental results demonstrate a full-lifecycle R² of 0.9936 and a mean absolute error (MAE) of 38.5136. While the early-stage R² of 0.6880 objectively reflects the statistical flatness of the equipment's healthy plateau, the framework maintains robust tracking capabilities across the entire lifespan, surpassing mainstream deep learning algorithms such as CNN, MLP, and LSTM. The proposed method consistently achieves a conservative, risk-averse predictive distribution for industrial reliability. Finally, model-level permutation importance analysis confirms that the RF model prioritizes physics-informed indicators rather than relying on spurious curve fitting.