Evaluation of proactive maintenance policies on a stochastically dependent hidden multi-component system using DBNs

In complex systems with stochastically dependent components which are not observed directly, determining an effective maintenance policy is a difficult task. In this paper, a dynamic Bayesian network based maintenance decision framework is proposed to evaluate proactive maintenance policies for such systems. Two preventive and one predictive maintenance strategies from a cost perspective are designed for multi-component dependable systems which aim to reduce maintenance cost while increasing system reliability at the same time. Tabu procedure is employed to avoid repetitive similar actions. The performances of the policies are compared with a reactive maintenance strategy and also with each other using different strategy parameters on a real life system confronted in thermal power plants for six different scenarios. The scenarios are designed considering different structures of system dependability and reactive cost. The results show that the threshold based maintenance which is the predictive strategy gives the minimum cost and maintenance number in almost all scenarios.

Automated summary

A dynamic Bayesian network-based maintenance decision framework was proposed to evaluate proactive maintenance policies for complex systems. Different scenarios in a real-life system were compared, showing that predictive maintenance strategy can provide the lowest cost and maintenance number in most cases.