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

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Elsevier Ltd

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Araştırma projeleri

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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.


This research is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant: 117M587.

Anahtar Kelimeler

Dynamic Bayesian networks, Multi-component hidden systems, Proactive maintenance, Stochastic dependency, Tabu procedure, Costs, Preventive maintenance, Stochastic systems, Tabu search, Thermoelectric power plants, Maintenance decisions, Maintenance policy, Maintenance strategies, Multi-component hidden system, Multi-component systems, Network-based, Stochastic dependencies, Bayesian networks, Opportunistic maintenance, Predictive maintenance, Control chart, Optimization, Strategies, Components, Failure, Model


Reliability Engineering and System Safety

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Özgür Ünlüakın, D. & Türkali, B. (2021). Evaluation of proactive maintenance policies on a stochastically dependent hidden multi-component system using DBNs. Reliability Engineering and System Safety, 211, 1-14. doi:10.1016/j.ress.2021.107559