Proposal of a new simulated annealing model-based fault identification technique applied to flight control EM actuators

In the last years the architecture of the actuation systems implemented in aeronautics in order to actuate the flight controls is changing radically and, as a consequence of the More Electric Aircraft paradigm, electromechanical actuators (EMAs) are gradually replacing the older type of actuators based on the hydraulic power. In order to detect incipient failures due to a progressive wear of a primary flight command EMA, prognostics could employ several approaches; the choice of the best ones is driven by the efficacy shown in failure detection, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. De-veloping a prognostic algorithm able to identify the precursors of the above mentioned EMAs faults and their degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew such to properly schedule the servomechanism replacement. The goal of this paper is to propose an innovative model-based fault detection and identification (FDI) method, based on Simulated Annealing (SA) Algorithms, investigating its ability to timely identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior. In particular, this work is focused on the analysis of progressive faults typically affecting the electrical components of the EMAs (e.g. electronic control, BLDC motor windings) and proposes a robust algorithm able to deal multiple faults. In order to assess the effectiveness of the proposed FDI technique, an appropriate simulation test environment was developed: two MATLAB Simulink models representing the real EMA and the corresponding monitor have been respectively used to simulate progressive failures and to evaluate the accuracy of this prognostic algorithm. The results showed an adequate robustness of the FDI technique and a satisfying confidence was gained about its ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures. This paper aims to be a starting point to future works based on this method for PHM applications.