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dc.contributor.authorLiang, Zen
dc.contributor.authorParlikad, Ajithen
dc.date.accessioned2019-10-21T23:30:32Z
dc.date.available2019-10-21T23:30:32Z
dc.date.issued2020-03-01en
dc.identifier.issn0951-8320
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/297957
dc.description.abstractPredictive maintenance has become highly popular in recent years due to the emergence of novel condition monitoring and data analysis techniques. However, the application of predictive maintenance at the network-level has not seen much attention in the literature. This paper presents a model for predictive group maintenance for multi-system multi- components networks (MSMCN). These networks are composed of multiple systems that are, in turn, composed of multiple components. In particular, the hierarchical structure of the MSMCN enables different representations of dependences at the network and system levels. The key novelty in the paper is that the designed approach combines analytical and numerical techniques to optimize the predictive group maintenance policy for MSMCNs. Moreover, we introduce a genetic algorithm with agglomerative mutation (GA-A) that enables a more effective evolution of the predictive group maintenance policy. Application of this model on a case study of a two-bridge network made of 23 different components shows a potential 11.27% reduction in maintenance cost, highlighting the model’s practical significance.
dc.description.sponsorshipThis research was funded by the Engineering and Physical Sciences Research Council (UK) and Innovate UK through the Innovation and Knowledge Centre for Smart Infrastructure and Construction (Grant EP/N021614/1). This work was partially supported by Talent recruitment Funds of Tsinghua University grant NO.1130521
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titlePredictive group maintenance for multi-system multi-component networksen
dc.typeArticle
prism.publicationDate2020en
prism.publicationNameReliability Engineering and System Safetyen
prism.volume195en
dc.identifier.doi10.17863/CAM.45011
dcterms.dateAccepted2019-10-20en
rioxxterms.versionofrecord10.1016/j.ress.2019.106704en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2020-03-01en
dc.contributor.orcidParlikad, Ajith [0000-0001-6214-1739]
dc.identifier.eissn1879-0836
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/N021614/1)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Societal Challenges (769255)
cam.orpheus.successThu Jan 30 10:38:26 GMT 2020 - Embargo updated*
rioxxterms.freetoread.startdate2021-03-01


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International