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dc.contributor.authorRødseth, Harald
dc.contributor.authorSchjølberg, Per
dc.contributor.authorMarhaug, Andreas
dc.date.accessioned2017-12-28T09:59:23Z
dc.date.available2017-12-28T09:59:23Z
dc.date.created2017-12-22T09:19:56Z
dc.date.issued2017
dc.identifier.citationAdvances in Manufacturing. 2017, 5 (4), 299-310.nb_NO
dc.identifier.issn2095-3127
dc.identifier.urihttp://hdl.handle.net/11250/2473800
dc.description.abstractWith the emergence of Industry 4.0, maintenance is considered to be a specific area of action that is needed to successfully sustain a competitive advantage. For instance, predictive maintenance will be central for asset utilization, service, and after-sales in realizing Industry 4.0. Moreover, artificial intelligence (AI) is also central for Industry 4.0, and offers data-driven methods. The aim of this article is to develop a new maintenance model called deep digital maintenance (DDM). With the support of theoretical foundations in cyber-physical systems (CPS) and maintenance, a concept for DDM is proposed. In this paper, the planning module of DDM is investigated in more detail with realistic industrial data from earlier case studies. It is expected that this planning module will enable integrated planning (IPL) where maintenance and production planning can be more integrated. The result of the testing shows that both the remaining useful life (RUL) and the expected profit loss indicator (PLI) of ignoring the failure can be calculated for the planning module. The article concludes that further research is needed in testing the accuracy of RUL, classifying PLI for different failure modes, and testing of other DDM modules with industrial case studies.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringernb_NO
dc.relation.urihttps://link.springer.com/article/10.1007/s40436-017-0202-9
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleDeep digital maintenancenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber299-310nb_NO
dc.source.volume5nb_NO
dc.source.journalAdvances in Manufacturingnb_NO
dc.source.issue4nb_NO
dc.identifier.doi10.1007/s40436-017-0202-9
dc.identifier.cristin1531321
dc.description.localcode© The Author(s) 2017. This article is an open access publication. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.nb_NO
cristin.unitcode194,64,92,0
cristin.unitnameInstitutt for maskinteknikk og produksjon
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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