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. Th