Model-based strategy and surrogate function for health condition assessment of actuation devices

Abstract

Prognostics and Health Monitoring (PHM) is a discipline aiming to determine in advance the Remaining Useful Life (RUL) of a system. To do so, the operation of the system is monitored in search of the early signs of degradation and incipient faults; then, a model for the propagation of faults is employed to estimate the propagation of damages and evaluate the RUL. Usually, a fault threshold is employed as a stopping criterion for the evaluation of damage propagation, but this is not a reliable method when dealing with multiple faults affecting the system at the same time. Specifically, the combined effect of two fault modes can cause the system not to meet its requirements well before the single faults reach their individual thresholds. In this work, we address a model-based strategy to estimate whether the system with incipient faults is still able to meet its performance requirements. The method is applied to aerospace actuators, and performance is evaluated in terms of dynamical response. This model-based algorithm is too slow to be evaluated in real-time, so a Support Vector Machine (SVM) is trained as a surrogate function to speed up the computation. The results and computational times of the full, physics based model and those of its surrogate are compared and discussed.