Health monitoring of electromechanical flight actuators via position-tracking predictive models

This article deals with the development and performance characterisation of model-based health monitoring algorithms for the detection of faults in an electromechanical actuator for unmanned aerial system flight controls. Two real-time executable position-tracking algorithms, based on predictors with different levels of complexity, are developed and compared in terms of false alarm rejection and fault detection capabilities, using a high-fidelity model of the actuator in which different types of faults are injected. The algorithms’ performances are evaluated by simulating flight manoeuvres with the actuator in normal operation as well as with relevant faults (motor coil faults, motor magnet degradation, voltage supply decrease). The results demonstrate that an accurate position-tracking monitor allows to obtain a prompt fault detection and fail-safe mode engagement, while more detailed monitoring functions can be used for fault isolation only