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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorSerradilla, Oscar
dc.contributor.authorZugasti, Ekhi
dc.contributor.authorZurutuza, Urko
dc.contributor.otherRodríguez Breton, Jon
dc.date.accessioned2022-03-14T14:17:03Z
dc.date.available2022-03-14T14:17:03Z
dc.date.issued2022
dc.identifier.issn0951-192Xen
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=167239en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/5495
dc.description.abstractThe 4th industrial revolution has connected machines and industrial plants, facilitating process monitoring and the implementation of predictive maintenance (PdM) systems that can save up to 60% of maintenance costs. Nowadays, most PdM research is carried out with expert systems and data-driven algorithms, but it is mainly focused on improving the results of reference simulation data sets. Hence, industrial requirements are not commonly addressed, and there is no guiding methodology for their implementation in real PdM use-cases. The objective of this work is to present a methodology for PdM application in industrial companies by combining data-driven techniques with domain knowledge. It defines sequentially ordered stages, steps and tasks to facilitate the design, development and implementation of PdM systems according to business and process characteristics. It also facilitates the collaboration among the required working profiles and defines deliverables. It is designed in a flexible and iterative way, combining standards, state-of-the-art methodologies and referent works of the field. Finally, the proposed methodology is validated on two use-cases: a bushing testbed and a press machine of the production line. These use-cases aim to facilitate, guide and speed up the implementation of the methodology on other PdM use-cases.en
dc.description.sponsorshipComisión Europeaes
dc.description.sponsorshipGobierno Vascoes
dc.description.sponsorshipDiputación Foral de Gipuzkoaes
dc.language.isoengen
dc.publisherTaylor and Francisen
dc.rights© 2022 Taylor and Francisen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectpredictive maintenanceen
dc.subjectmethodologyen
dc.subjectdata-drivenen
dc.subjectdomain knowledgeen
dc.subjectmanufacturingen
dc.titleMethodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledgeen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/embargoedAccessen
dcterms.sourceInternational Journal of Computer Integrated Manufacturingen
dc.description.versioninfo:eu-repo/semantics/acceptedVersionen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1080/0951192X.2022.2043562en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/825030/EU/Digital Reality in Zero Defect Manufacturing/QU4LITYen
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Ikertalde Convocatoria 2019-2021/IT1357-19/CAPV/Sistemas Inteligentes para Sistemas Industriales/en
local.relation.projectIDinfo:eu-repo/grantAgreement/DFG/Programa de Red Guipuzcoana de Ciencia, Tecnología e Innovación 2020/OF-326-2020/GIP/Hacia una metodología que guíe a la industria al mantenimiento predictivo y explicativo/MEANERen
local.embargo.enddate2023-03-31
local.contributor.otherinstitutionKoniker, S. Coop.es
local.source.detailsPublished online: 02 Mar 2022en


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