Author： Gruber Aviv Yanovski Shai Ben-Gal Irad
Publisher： Taylor & Francis Ltd
ISSN： 0898-2112
Source： Quality Engineering, Vol.25, Iss.4, 2013-10, pp. : 370-384
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Abstract
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.
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