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An approach to improve asset maintenance and management priorities using machine learning techniques

dc.contributor.authorHanish Nithin, Anu
dc.contributor.authorHobbs, Michael
dc.contributor.authorSriramula, Srinivas
dc.contributor.authorSripada, Somayajulu
dc.date.accessioned2024-05-08T23:16:43Z
dc.date.available2024-05-08T23:16:43Z
dc.date.issued2022-11-23
dc.identifier220121787
dc.identifier55f1240a-50da-4a5d-ba4b-341dbfdea0e4
dc.identifier.citationHanish Nithin , A , Hobbs , M , Sriramula , S & Sripada , S 2022 , ' An approach to improve asset maintenance and management priorities using machine learning techniques ' , Safety and Reliability , vol. 41 , no. 3 , pp. 151-169 . https://doi.org/10.1080/09617353.2022.2142011en
dc.identifier.issn0961-7353
dc.identifier.otherORCID: /0000-0002-5428-8383/work/131351059
dc.identifier.otherORCID: /0000-0001-6158-8686/work/131351289
dc.identifier.urihttp://aura-test.abdn.ac.uk/handle/2164/20291
dc.descriptionOpen Access via T&F Agreement Funding: This research project was funded and supported by Innovate UK and Optimal (Optima Asset Maintenance Solutions Ltd.) as part of the Knowledge Transfer Partnerships Scheme.en
dc.format.extent19
dc.format.extent3350864
dc.language.isoeng
dc.relation.ispartofSafety and Reliabilityen
dc.subjectasset managementen
dc.subjectmachine learningen
dc.subjectcriticality analysisen
dc.subjectpredictive maintenanceen
dc.subjectreliability engineeringen
dc.subjectdeep learningen
dc.subjectTA Engineering (General). Civil engineering (General)en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject.lccTAen
dc.subject.lccQA75en
dc.titleAn approach to improve asset maintenance and management priorities using machine learning techniquesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Engineeringen
dc.contributor.institutionUniversity of Aberdeen.Engineeringen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Computational Linguistics at Aberdeenen
dc.description.statusPeer revieweden
dc.identifier.doihttps://doi.org/10.1080/09617353.2022.2142011


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