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dc.contributor.authorMARSH, DWR
dc.contributor.authorZhang, H
dc.date.accessioned2017-10-27T10:17:37Z
dc.date.available2017-10-27T10:17:37Z
dc.date.issued2017
dc.date.submitted2017-10-10T12:42:53.699Z
dc.identifier.citationMARSH, D. and Zhang, H. (2017). Generic Bayesian Network Models for Making Maintenance Decisions from Available Data and Expert Knowledge. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability.en_US
dc.identifier.issn1748-006X
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/28444
dc.description.abstractTo maximize asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. Various statistical models have been proposed for predicting this but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how i) data on the condition of assets available from their periodic inspection can be used ii) failure data from related groups of asset can be combined using judgement from experts iii) expert knowledge of the causes deterioration can be combined with statistical data to adjust predictions. A case study of bridges on the GB rail network is presented, showing how the model could be used for the maintenance decision problem, given typical data likely to be available in practice.en_US
dc.publisherSAGE Publicationsen_US
dc.relation.ispartofProceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
dc.rightsThis is a pre-copyedited, author-produced version of an article accepted for publication in Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability following peer review.
dc.titleGeneric Bayesian Network Models for Making Maintenance Decisions from Available Data and Expert Knowledgeen_US
dc.rights.holder© 2017 SAGE Publications
pubs.organisational-group/Queen Mary University of London
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering/Electronic Engineering and Computer Science - Staff
pubs.publication-statusAccepted


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