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dc.creatorIzquierdo, Juanes
dc.creatorCrespo Márquez, Adolfoes
dc.creatorUribetxebarria, Jonees
dc.date.accessioned2020-03-20T10:50:34Z
dc.date.available2020-03-20T10:50:34Z
dc.date.issued2019-08
dc.identifier.citationIzquierdo, J., Crespo Márquez, A. y Uribetxebarria, J. (2019). Dynamic artificial neural network-based reliability considering operational context of assets. Reliability Engineering & System Safety, 188 (August), 483-493.
dc.identifier.issn0951-8320es
dc.identifier.urihttps://hdl.handle.net/11441/94380
dc.descriptionPostprint. 24 meses de embargo (Elsevier)es
dc.description.abstractAssets reliability is a key issue to consider in the maintenance management policy and given its importance several estimation methods and models have been proposed within the reliability engineering discipline. However, these models involve certain assumptions which are the source of different uncertainties inherent to the estimations. An important source of uncertainty is the operational context in which the assets operate and how it affects the different failures. Therefore, this paper contributes to the reduction of the uncertainty coming from the operational context with the proposal of a novel method and its validation through a case study. The proposed model specifically addresses changes in the operational context by implementing dynamic capabilities in a new conception of the Proportional Hazards Model. It also allows to model interactions among working environment variables as well as hidden phenomena thanks to the integration within the model of artificial neural network methodses
dc.formatapplication/pdfes
dc.format.extent11 p.es
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofReliability Engineering & System Safety, 188 (August), 483-493.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDynamic reliabilityes
dc.subjectProportional hazards modeles
dc.subjectArtificial neural networkses
dc.subjectOperational contextes
dc.subjectMaintenance managementes
dc.subjectEpistemic uncertaintyes
dc.titleDynamic artificial neural network-based reliability considering operational context of assetses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/OpenAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas Ies
dc.relation.projectIDH2020-MSCA465 RISE-2014es
dc.relation.projectIDEmaitekPlus 2018-2019es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/abs/pii/S0951832018311906es
dc.identifier.doi10.1016/j.ress.2019.03.054es
dc.contributor.groupUniversidad de Sevilla. TEP134: Organizacion Industriales
dc.journaltitleReliability Engineering & System Safetyes
dc.publication.volumen188es
dc.publication.issueAugustes
dc.publication.initialPage483es
dc.publication.endPage493es
dc.contributor.funderGobierno Vasco. EmaitekPlus 2018-2019 Programes
dc.contributor.funderHorizon 2020, MSCA-RISE-2014: Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) (grant agreement number 645733 — Sustain-Owner — H2020-MSCA-RISE-2014)es

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