Electrohydraulic Servomechanisms Affected by Multiple Failures: A Model-Based Prognostic Method Using Genetic Algorithms

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 new prognostic procedure centered on the characterization of the state of health of a common electro-hydraulic actuator for primary command usage. It is based on an innovative model based fault detection and identification method (FDI) that makes use of deterministic and heuristic solvers in order to converge to the actual state of wear of the tested actuator. In particular, the proposed model takes in account three different types of progressive failures: the clogging of the first stage of the flapper-nozzle valve, the rising of friction between spool and sleeve and finally the rising of friction between jack and cylinder. 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