Monitoring based on Analytical Redundancy and Classification for a Primary Flight Surface Electromechanical Actuator

Our objective is to develop a monitoring algorithm that achieves early detection of degradations that may lead to jamming or excessive backlash in the electromechanical actuators (EMAs) used for primary fight control. To do so, a detailed EMA model is determined in order to construct a simulator able to reproduce test bench results and simulate faulty behaviours. Exploiting the analytical redundancy within the dynamical model of the EMA, a residual generator is designed to be sensitive to a potential friction change or backlash increase, by using the typical measurement sequences associated to pre-flight test profiles. A classifier based on a multiclass Support Vector Machine approach is used to detect and identify the nature of the faults. The classifier validity is corroborated by a K-fold cross-validation. Finally, tests are conducted on the simulator under various friction and backlash situations. The tests are performed under faults of realistic amplitude while considering uncertainties in the model parameters. The developed algorithm ensures the identification of the change in the friction and/or backlash with a successful classification rate of 98.13%.