Health Status Assessment of Unmanned Aerial Vehicle Engine Based on AHP Enhancement and Multimodal Fusion

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.