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Predictive maintenance of complex system with multi-level reliability structure
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DOI：10.1080/00207543.2017.1299947.png)
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摘要
En 中文
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.
Keyword:
Reliability evaluation
Markov modelling
maintenance planning
Bayesian network
predictive maintenance
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期刊
IF:
7.3
论文数:
1.1W
被引数:
3.7W
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