Learning for Predictions: Real-Time Reliability Assessment of Aerospace Systems

Prognostics and health management aim to predict the remaining useful life (RUL) of a system and to allow a timely planning of replacement of components, limiting the need for corrective maintenance and the downtime of equipment. A major challenge in system prognostics is the availability of accurate physics-based representations of the faults dynamics. Additionally, the analysis of data acquired during flight operations is traditionally time consuming and expensive. This work proposes a computational method to overcome these limitations through the dynamic adaptation of the state-space model of fault propagation to onboard observations of the system's health. Our approach aims at enabling real-time assessment of systems' health and reliability through fast predictions of the remaining useful life that accounts for uncertainty. The strategy combines physics-based knowledge of the system damage propagation rate, machine learning. and real-time measurements of the health status to obtain an accurate estimate of the RUL of aerospace systems. The original method is demonstrated for the RUL prediction of an electromechanical actuator for aircraft flight controls. We observe that the strategy allows us to refine rapid predictions of the RUL in fractions of seconds by progressively learning from onboard acquisitions.