Abstract
In order to identify incipient failures due to a progressive wear of a primary flight command electromechanical actuator, several approaches could be employed; the choice of the best ones is driven by the efficacy shown in fault detection/identification, 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 abovementioned electromechanical actuator (EMA) failure and its degradation pattern is thus beneficial for anticipating the incoming malfunction and alerting the maintenance crew such to properly schedule the servomechanism replacement. The research presented in the paper was focused to develop a fault detection/identification technique, able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior, and to evaluate its potential use as prognostic indicator for the considered progressive faults (i.e. frictions and mechanical backlash acting on transmission, stator coil short circuit, rotor static eccentricity). To this purpose, an innovative model based fault detection technique has been developed merging several information achieved by means of Fast Fourier Transform (FFT) analysis and proper "failure precursors" (calculated by comparing the actual EMA responses with the expected ones). To assess the performance of the proposed technique, an appropriate simulation test environment was developed: the results showed an adequaterobustness and confidence was gained in the ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures.
Highlights
As defined by Vachtsevanos, Lewis, Roemer, Hess, & Wu (2006), the purpose of prognostics is to predict accurately the Remaining Useful Life (RUL) of a failing component or subsystem
The data representing the dynamic response of the actual system are compared with the results provided by the monitoring system: the more the fault is considerable, the more the results obtained from the simulated actual system differ from the theoretical data
After the analysis performed on a single progressive fault, this work focuses on the effects due to the simultaneous presence of different kinds of faults acting on the system
Summary
As defined by Vachtsevanos, Lewis, Roemer, Hess, & Wu (2006), the purpose of prognostics is to predict accurately the Remaining Useful Life (RUL) of a failing component or subsystem. The primary flight controls are a critical feature of the aircraft system and are designed with a conservative safe-life approach, which imposes to replace the related components subsequently to a certain number of flight hours (or operating cycles) This approach fails in the detection of possible initial flaws, due to the manufacturing process, that could generate a sudden failure, which could compromise the safety of the aircraft. In order to fulfill these two objectives, an innovative model-based fault detection and identification technique has been developed merging several information achieved by means of FFT analysis and proper failure precursors, i.e. system parameters whose variations could be associated with specific impending failure (Vichare & Pecht 2006) This technique was tested by means of numerical simulations on a typical aircraft primary command EMA, modeled in the MATLAB Simulink® simulation environment. The obtained maps were utilized for the successful evaluation of the damage level affecting the EMA
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