Context-Adaptive and Physics-Consistent Constrained Multimodal Interpretable Remaining Useful Life Prediction

Remaining useful life (RUL) prediction for complex equipment is a critical technology for ensuring the safe and reliable operation of industrial systems. However, existing data-driven models commonly suffer from limitations such as weak cross-operational condition generalization, insufficient physical interpretability, and unstable training on non-stationary time-series data. To address these challenges, this paper proposes a temporal degradation prediction model that integrates context adaptation and physics-consistent constraints, named the Context-Adaptive Physics-informed Time-aware meta-Network (CAPTAIN). The model incorporates four core components: a Context-Aware Meta-Learning (CAML) module that enables lightweight parameter adaptation to diverse scenarios; Physics-Informed Neural Network (PINN) constraints that uniformly characterize deterministic degradation dynamics and stochastic Wiener process perturbations; a three-layer dynamic stabilization training strategy comprising temporal meta-training, residual adaptive refinement, and exponential moving average to ensure training stability; and a multimodal interpretability framework integrating LIME, GradCAM, GradCAM_LW, Integrated Gradients, and KernelSHAP to enhance prediction transparency. Extensive experiments on the NASA C-MAPSS datasets (FD001–FD004) demonstrate that CAPTAIN achieves state-of-the-art performance under both single/multiple failure modes and steady/varying operating conditions, with an average RMSE of 12.02 ± 0.98 and an average SCORE of 487.50 ± 23.0, outperforming ten advanced baseline models. The model exhibits exceptional generalization capability across different operational conditions and strong robustness in scenarios with coupled multiple faults. Multimodal visualizations and quantitative assessments verify its interpretability advantages, showing high consistency with the physical degradation laws of engines. This work provides a reliable paradigm for RUL prediction of complex equipment, combining the flexibility of data-driven modeling with the credibility of physical modeling.