Abstract
Several approaches can be employed in prognostics, to detect incipient failures of primary flight command electromechanical actuators (EMA), caused by progressive wear. The development of a prognostic algorithm capable of identifying the precursors of an electromechanical actuator failure is beneficial for the anticipation of the incoming failure: a correct interpretation of the failure degradation pattern, in fact, can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. Prognostic, though, is strictly technology-oriented as it is based on accurate analysis of the cause and effect relationships. As a consequence, it is possible that prognostics algorithms that demonstrate great efficacy for certain applications (electrohydraulic actuators, for examples) fail in other circumstances, just because the actuator is based on a different technology. The research presented in this paper proposes a prognostic technique able to identify symptoms of an EMA degradation before the actual exhibition of the anomalous behavior; to this purpose friction, backlash, coil short circuit and rotor static eccentricity failures are considered. An innovative model-based fault detection neural technique is proposed to analyze information gathered through FFT analysis of the components under normal stress conditions. A proper simulation test bench was developed: results show that the method exhibit adequate robustness and a high degree of confidence in the ability to early identify an eventual malfunctioning, minimizing the risk of false alarms or unannunciated failures.
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