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.

With the growing deployment of unmanned aerial vehicles (UAVs), reliable engine health state assessment (HSA) requires methods that are interpretable, auditable, and transferable under noisy data and varying operating conditions. This paper proposes an AHP-enhanced, data-driven HSA framework that builds a unified health vector from four indicators—remaining useful life (RUL) health, absolute state, relative degradation, and condition health. Indicator weights are derived using AHP with consistency checking, and the resulting continuous health index is mapped through nonlinear stretching and four-level thresholds to produce actionable health grades. Experiments on the NASA CMAPSS benchmark (FD001) evaluate conventional machine-learning models (e.g., XGBoost, SVM, Random Forest, MLP, Logistic) and temporal deep models (CNN-LSTM, Keras DNN). Results show that injecting AHP indicators consistently improves classification performance across models; in particular, AHP-CNN-LSTM achieves 0.92 accuracy and 0.924 Macro-F1, outperforming the CNN-LSTM baseline (0.88/0.872). SHAP-based analysis further supports the contribution of key indicators/features to separating adjacent degradation levels. Additional experiments on FD002–FD004 demonstrate the framework’s applicability under multi-operating-condition settings, providing practical guidance for UAV engine prognostics and health management.