Abstract

Modern systems, that are often constituted of different interacting components, have increasing requirements for improving availability and safety when suffering unanticipated failures. Therefore, one needs to continuously monitor them, estimate and predict their health states through the implementation of prognostics and health management processes. To make this estimation and prediction more relevant and accurate, different issues, such as the component interactions, the profile mission impacts, and the uncertainties, should be considered. Compared to component level, these issues are more challenging in system-level prognostics. Their resolution is then essential to improve reliability, accuracy, and precision of the prognostics results. In this article, the problems of component interactions, profile mission impacts, and uncertainty quantification in system-level prognostics are addressed. For this purpose, a method using the modeling framework of the inoperability input-output model and composed of three steps is proposed. First, the system inoperability is estimated using particle filtering while considering the interactions between its components. Then, the estimated inoperability is propagated for long-term predictions. Finally, the system remaining useful life is calculated depending on the system configuration. The proposed methodology is applied to a mechatronic system to show its effectiveness.