Condition-based maintenance via a targeted bayesian network meta-model

Aviv Gruber, Shai Yanovski, Irad Ben-Gal

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This chapter suggests a condition-based maintenance (CBM)-based policy that combines both a simulation model of the system and a predictive meta-model. The simulation model of the system is based on expert knowledge and historical data. The chapter overviews the challenges of preventive maintenance optimization in reliability-availability-maintainability models and refers to several methods for addressing these challenges. The chapter also describes targeted Bayesian Network learning and the motivation for using a Bayesian Network as a meta-model for prediction. The chapter then provides a schematic framework for the implementation of the proposed CBM approach. It demonstrates the implementation of the proposed approach on a freight rail fleet based on a real case study of a European operator. The chapter further analyzes some key features of the proposed approach. It concludes with a short discussion on potential future directions.

Original languageEnglish
Title of host publicationSystems Engineering in the Fourth Industrial Revolution
Subtitle of host publicationBig Data, Novel Technologies, and Modern Systems Engineering
Number of pages24
ISBN (Electronic)9781119513957
ISBN (Print)9781119513896
StatePublished - 24 Jan 2020


  • Condition-based maintenance
  • Corrective maintenance
  • European operator
  • Predictive meta-model
  • Preventive maintenance
  • Reliability-availability-maintainability models
  • Simulation model
  • Targeted bayesian network meta-model


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