Study of innovative model-based prognostic algorithms applied to aerospace electromechanical actuators

The prior knowledge of incipient failures of primary flight command electromechanical actuators (EMAs) with prognostic algorithms can be very beneficial. Indeed, an early and proper detection and interpretation of the deterioration pattern can warn for the replacement of the servomechanism before the actual manifestation of the abnormal behavior. Such algorithms often exploit a model-based approach established on the direct comparison between the real (High Fidelity) system and the monitor (Low Fidelity) system to identify fault parameters through optimization processes, with the monitor model allowing to acquire accurate and precise results with a contained computational effort. To this purpose, the thesis aims at presenting a prognostic technique consisting of a simplified monitor model capable of faithfully reproducing the dynamic response of the reference model in terms of position, speed and equivalent current, taking into account the presence of several mechanical and electrical faults: friction, backlash, coil short circuit, static rotor eccentricity, and proportional gain. Fault detection and isolation is performed by comparing the output signal of the reference system with the one obtained from the monitor model. After that, the Genetic Algorithm is chosen as the optimization algorithm to match the two signals by iteratively changing the fault parameters to detect the global minimum of a quadratic error function. Once a suitable fit is obtained, the corresponding parameters are assumed to be acceptable. The reference models analyzed in this work have been previously developed in Matlab-Simulink by researchers of the ASTRA Group of the Department of Mechanical and Aerospace Engineering of the Politecnico di Torino.