In order to detect incipient failures due to a progressive wear of a primary flight command electromechanical actuator, 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 fault detection algorithm able to identify the precursors of the above mentioned electromechanical actuator (EMA) failure and its degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew such to properly schedule the servomechanism replacement. The research presented in the paper was focused to develop a prognostic technique, able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior; in particular four kinds of failure are considered: friction, backlash, coil short circuit, rotor static eccentricity. To this purpose, an innovative model based fault detection technique has been developed
merging together several information achieved by means of FFT analysis and proper "failure precursors" (calculated by comparing the actual EMA responses with the expected ones). To assess the robustness of the proposed technique, an appropriate simulation test environment was developed. 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.
How to Cite
EMA, FFT, aircraft system, primary flight command
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