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.

预测和健康管理旨在预测系统的剩余使用寿命 (RUL)，并允许及时规划组件更换，限制纠正性维护和设备停机时间的需要。系统预测的一个主要挑战是故障动态的基于物理的准确表示的可用性。此外，飞行操作期间获取的数据分析传统上既耗时又昂贵。这项工作提出了一种计算方法，通过故障传播的状态空间模型动态适应系统健康的机载观察来克服这些限制。我们的方法旨在通过对不确定性的剩余使用寿命进行快速预测，从而对系统的健康和可靠性进行实时评估。该策略结合了基于物理的系统损伤传播率知识和机器学习。和实时测量健康状况，以获得对航空航天系统 RUL 的准确估计。演示了原始方法用于飞机飞行控制的机电执行器的 RUL 预测。我们观察到，该策略允许我们通过逐步从机载采购中学习，在几分之一秒内改进对 RUL 的快速预测。