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dc.contributor.authorTurner, C
dc.contributor.authorOkorie, O
dc.contributor.authorOyekan, J
dc.date.accessioned2022-10-03T07:52:38Z
dc.date.issued2022-09-27
dc.date.updated2022-10-01T16:26:40Z
dc.description.abstractThe field of Explainable Artificial Intelligence (XAI) is a relatively new approach to AI, with the aim to provide black box algorithms with human intelligible narrative functionality. It is most often in end-of-life considerations of the asset lifecycle that sustainability issues are encountered. Modern maintenance practice requires a holistic understanding of lifecycle and options for sustainable asset treatments. human in the loop solutions offer a way to leverage both machine and human skill sets to provide the next level of automaton solutions for industrial maintenance activities. This paper presents a framework for human in the loop Intelligent and Sustainable Maintenance. In bridging the gap between machines and humans XAI leverages the best of both worlds to provide a new level of agility to cyber assisted maintenance activities and full lifecycle consideration of assets; a notion that is necessary throughout the organization in the achievement of sustainability goals set by governments around the world in the achievement of a net zero carbon emission economy.en_GB
dc.format.extent67-72
dc.identifier.citationVol. 55(19), pp. 67-72en_GB
dc.identifier.doihttps://doi.org/10.1016/j.ifacol.2022.09.185
dc.identifier.urihttp://hdl.handle.net/10871/131060
dc.identifierORCID: 0000-0002-5210-9951 (Okorie, Okechukwu)
dc.language.isoenen_GB
dc.publisherElsevier / International Federation of Automatic Control (IFAC)en_GB
dc.rights© 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)en_GB
dc.subjectHuman in the loopen_GB
dc.subjectIntelligent maintenance systemsen_GB
dc.subjectIndustry 5.0en_GB
dc.subjectPredictive maintenanceen_GB
dc.subjectOperator 5.0en_GB
dc.titleXAI Sustainable Human in the Loop Maintenanceen_GB
dc.typeArticleen_GB
dc.date.available2022-10-03T07:52:38Z
dc.identifier.issn2405-8963
dc.descriptionThis is the final version. Available on open access from Elsevier via the DOI in this recorden_GB
dc.identifier.journalIFAC-PapersOnLineen_GB
dc.relation.ispartofIFAC-PapersOnLine, 55(19)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_GB
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2022-01-01
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-10-03T07:50:08Z
refterms.versionFCDVoR
refterms.dateFOA2022-10-03T07:53:54Z
refterms.panelBen_GB


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© 2022 The Authors. This is an open access article under the CC BY-NC-ND license
(https://creativecommons.org/licenses/by-nc-nd/4.0/)
Except where otherwise noted, this item's licence is described as © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)