Abstract
Models of maintenance problems must handle complex assumptions, allowing, for example, the condition of some assets to be rated directly using multiple states while in others the condition rating is inferred from that of the components from which they are assembled. The overall condition inferred, which informs the maintenance decisions, requires evidential reasoning under uncertainty. This paper uses Bayesian networks to address these challenges with real case studies. We apply the binary factorisation technique to allow inference of multi-state condition prediction, and further extend it to predict the condition of an asset with multiple components. These models are used to recommend inspection decisions such as which assets to inspect and when to inspect them. Models are also developed to evaluate the effectiveness of repair interventions and to use this to suggest repair actions. We show how to model multiple interventions within the asset life cycle considering both repair effectiveness and further deterioration. This modelling allows us to plan maintenance activities for an asset over its whole life cycle.
Submitted Version (
Free)
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
