Condition-based maintenance via simulation and A targeted bayesian network metamodel

Aviv Gruber, Shai Yanovski, Irad Ben-Gal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Condition-based maintenance (CBM) is increasingly applied to operational systems to reduce lifecycle costs. Predicting the performance of various CBM policies is a challenging task addressed in this work. We suggest a CBM framework that is based on system simulations and a targeted Bayesian network model. Simulations explore the robustness of various CBM policies under different scenarios. The Bayesian network, which is learned from the simulation data, is then used as an explanatory compact metamodel for failure prediction. The framework is demonstrated through a study of an operator of a freight rail fleet. This study demonstrates a significant profit improvement compared to other methods.

Original languageEnglish
Pages (from-to)370-384
Number of pages15
JournalQuality Engineering
Issue number4
StatePublished - 1 Oct 2013


FundersFunder number
Israel Ministry of Science, Israel Scientifci Foundation
Israeli Prime Minister Ministry
IEEE Foundation
General Motors of Canada
European Commission
Israel Science Foundation


    • Bayesian networks
    • condition-based maintenance
    • meta-model
    • simulation


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