Enhanced hybrid prognostic approach applied to aircraft on-board electromechanical actuators affected by progressive faults

In the last generation aircraft, the architecture of the powered flight control system adopted Electromechanical Actuators (EMAs). Being some on-board actuator safety critical, the practice of monitoring their behavior to determine their health condition is a task of growing importance. The choice of the best prognostic algorithm is driven primarily by their effectiveness in correctly identifying the health conditions of the system since each technique might be more or less useful in a given situation. In this contest, the authors propose a new GA-based fault detection tool, relying on a model-based approach, comparing the system output to that of a Monitor Model (MM), which is able to reproduce accurately the dynamic response of the actual EMA in terms of position, speed and equivalent current, even under the effects of different failure modes while keeping a reasonably low computational cost; this Fault Detection and Identification (FDI) algorithm have been extended to seven progressive failures. A numerical simulation test environment has been developed to simulate progressive faults and to evaluate the accuracy of this prognostic method. Results showed an adequate robustness and a suitable ability to early identify malfunctions with low risk of false alarms or missed failures. Moreover, the effect of a failures different from those considered was studied, to avoid safety concerns related to the missed identification of an incipient failure, hidden by another unknown failure mode