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Condition-Based Maintenance via Simulation and a Targeted Bayesian Network Metamodel

Summary: [This abstract is based on the authors' 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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  • Topics: Quality Control
  • Keywords: Bayesian methods, Maintenance, Failure, Case study, Railroads, Transportation industry
  • Author: Gruber, Aviv; Yanovski, Shai; Ben-Gal, Irad;
  • Journal: Quality Engineering