Evaluating reliability of complex systems for Predictive maintenance

TL;DR

This paper introduces a probabilistic framework combining Discrete Time Markov Chains and Bayesian Networks to improve predictive maintenance for complex systems with uncertain reliability structures.

Contribution

It develops a novel PdM scheme that models component health and system reliability probabilistically, addressing uncertainties in complex systems.

Findings

Effective probabilistic inference for system and component health.

Enhanced scheduling of predictive maintenance based on reliability predictions.

Addresses limitations of deterministic models in complex systems.

Abstract

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.