Keynote speech: Risk-based Prognostics and Health Management: Probabilistic methods for continuous-time hazard analysis and risk mitigation

Abstract

To date, most practical implementations of Prognostics and Health Management (PHM) have focused on the health management aspects with only minor attention being given to fault prediction. Commercial and military aerospace systems often employ a wide variety of embedded and on-board sensors to track and estimate the health of systems, and this information is being employed in frameworks such as reliability centered maintenance and condition based maintenance. Recently, organizations such as NASA and the US Army have started to explore incorporating formal methods for online risk assessment to guide interpreting health assessments and making system maintenance decisions. In this talk, I will introduce work being performed at Montana State University where we are coupling traditional model-based diagnostic and health assessment methodologies with continuous-time probabilistic methods to track and predict the impact of emerging hazards in a system using real-time, condition-based assessments of system health and the emergence of likely faults. Specifically, I will address how we have integrated dependency-based methods for fault diagnosis with fault tree analysis to derive and use continuous-time Bayesian networks as an end-to-end approach in real-time monitoring, tracking, predicting, and mitigating risks associated with system failure.