Parametric Methods for Fault Analysis Applied to a Servomechanism Affected by Multiple Failures

In order to detect incipient failures due to a progressive wear of a primary flight command electro hydraulic actuator (EHA), 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 EHA failure and its degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew so as 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 EHA component is degrading and will eventually exhibit an anomalous behavior; in particular, six different types of progressive failures are considered (dry friction acting of servovalve spool or mechanical actuator, radial clearance between spool and sleeve, shape of the corners of the spool lands, torque sensitivity of the first stage torque motor, contamination of the first stage filter). To this purpose, an innovative model based fault detection technique, based upon “failure maps”, has been developed merging together the information achieved by FFT analysis and proper "failure precursors" (calculated comparing the actual EHA 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 EHA malfunctioning with low risk of false alarms or missed failures.