Predictive maintenance of complex system with multi-level reliability structure

Abstract

Onboard sensors, which constantly monitor the states of a system and its components, have made the predictive maintenance (PdM) of a complex system possible. To date, system reliability has been extensively studied with the assumption that systems are either single-component systems or they have a deterministic reliability structure. However, in many realistic problems, there are complex multi-component systems with uncertainties in the system reliability structure. This paper presents a PdM scheme for complex systems by employing discrete time Markov chain models for modelling multiple degradation processes of components and a Bayesian network (BN) model for predicting system reliability. The proposed method can be considered as a special type of dynamic Bayesian network because the same BN is repeatedly used over time for evaluating system reliability and the inter-time–slice connection of the same node is monitored by a sensor. This PdM scheme is able to make probabilistic inference at any system level, so PdM can be scheduled accordingly.

Keywords:

• Reliability evaluation
• Markov modelling
• maintenance planning
• Bayesian network
• predictive maintenance

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Division of Civil, Mechanical and Manufacturing Innovation [grant number 1301075].