Particle Filter-Based Prognosis and Health Monitoring of Electromechanical Actuators

Given the important role of Electromechanical Actuators (EMAs) in the aviation industry, this paper aims to develop Prognosis and Health Monitoring (PHM) solutions for EMAs. We begin by analyzing the general configuration and architecture of EMAs and demonstrate that load torque oscillation induces amplitude modulation in the stator current. We also propose a relationship between two faults, namely spiral bevel gear and flex spline wear. Next, we model an EMA, including a brushless DC motor, inverter, gearbox, mechanical load, and other units. As a prerequisite for fault prediction, we address the estimation of two states: stator current and motor speed. We use a Particle Filter-based (PF) methodology to estimate these states and perform predictions. The prediction scheme involves forming an auxiliary state corresponding to fault degradation, based on which the remaining useful life (RUL) of the system is computed. Finally, we present extensive simulation results of the proposed methodology corresponding to various scenarios.