Proposal of prognostic parametric method applied to an electrohydraulic servomechanism affected by multiple failures

Prognostics could employ several approaches with the aim to detect incipient failures due to a progressive wear of a primary flight command electro hydraulic actuator (EHA); the efficacy shown in failure detection drives the choice of the best ones, since not all the algorithms might be useful for the intended purpose. This happens because some of them could be suitable only for specific applications while giving bad results for others. The development of a fault detection algorithm is thus beneficial for anticipating the incoming failure and alerting the maintenance crew so as to properly schedule the servomechanism replacement; such algorithm should be able to identify the precursors of the above mentioned EHA failure and its degradation pattern. This paper presents a research focused on the development of a prognostic methodology, able to identify symptoms alerting that an EHA component is degrading and will eventually exhibit an anomalous behavior; in detail, six different types of progressive failures have been 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 achieve such objectives, an innovative model based fault detection technique 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), relying upon a set of failure maps. The robustness of the proposed technique has been assessed through a simulation test environment, built on the purpose. Such simulation has demonstrated that the methodology has adequate robustness; also, the ability to early identify an eventual malfunctioning has been proved with low risk of missed failures or false positives.