Explainable machine learning for condition monitoring of aircraft electromechanical actuators under variable loads: health state diagnosis from multi-sensor dynamic responses

Electro-Mechanical Actuators (EMAs) are being adopted in small-aircraft primary flight-surface control due to their compactness and efficiency; however, their mechanical transmission components remain vulnerable to progressive degradation under variable loads and harsh operating conditions. This study proposes an explainable machine learning framework for health-state diagnosis of aircraft EMAs using multi-sensor dynamic responses acquired from the EU-H2020 REPRISE endurance campaign dataset. The approach is aligned with applied mechanics by exploiting degradation-sensitive dynamic signatures extracted from both electrical and mechanical domains, including three-phase motor currents, load and temperature measurements, and vibration-related indicators derived from position signals via numerical differentiation. To ensure interpretability and robust feature relevance assessment, SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of each sensor variable to the diagnostic decision process and to identify the most informative features. Based on SHAP-driven feature ranking, the diagnostic model is refined to primarily utilize current- and vibration-related features, which exhibit the highest sensitivity to friction evolution, load-dependent nonlinearities, and transmission wear. Four machine learning classifiers—k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and a Deep Neural Network (DNN)—are trained and used. Comparative evaluation demonstrates that RF and DNN achieve superior classification accuracy, reaching CA = 96.4% and CA = 97.8%, respectively, while SVM and kNN yield competitive performance with CA = 94.1% and CA = 91.6%. The results confirm that explainable AI enhances diagnostic reliability and provides mechanics-consistent insights into the degradation process that support the development of interpretable and scalable health monitoring strategies for next-generation aerospace electromechanical systems.