Evaluating reliability of complex systems for Predictive maintenance
It addresses the need for predictive maintenance in multi-component systems with uncertain reliability structures, which is a practical problem in industrial settings.
This paper develops a predictive maintenance scheme for complex systems with uncertain reliability structures by combining Discrete Time Markov Chains for component health forecasting and Bayesian Networks for system-level reliability modeling, enabling probabilistic inferences for maintenance scheduling.
Predictive Maintenance (PdM) can only be implemented when the online knowledge of system condition is available, and this has become available with deployment of on-equipment sensors. To date, most studies on predicting the remaining useful lifetime of a system have been focusing on either single-component systems or systems with deterministic reliability structures. This assumption is not applicable on some realistic problems, where there exist uncertainties in reliability structures of complex systems. In this paper, a PdM scheme is developed by employing a Discrete Time Markov Chain (DTMC) for forecasting the health of monitored components and a Bayesian Network (BN) for modeling the multi-component system reliability. Therefore, probabilistic inferences on both the system and its components status can be made and PdM can be scheduled on both levels.