In the last years, Electro-Mechanical 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. Developing 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 modelbased fault detection and identification (FDI) method, based on Genetic Algorithms (GA), able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior; in particular four kinds of EMA progressive fault are considered: friction, backlash, coil short circuit and electronics fault of controller. To assess the effectiveness of the proposed technique, an appropriate simulation test environment was
developed: in particular, two MATLAB Simulink models representing the real EMA and the corresponding monitor have been used to simulate failures and evaluate the accuracy of the FDI algorithm. The results showed an adequate robustness and confidence was gained in the 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.
How to Cite
Electromechanical actuator, model based prognostics, EMA, genetic algorithm, Fault identification, BLDC Motor Faults
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