Por motivos de mantenimiento se ha deshabilitado el inicio de sesión temporalmente. Rogamos disculpen las molestias.

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dc.creatorCrespo Márquez, Adolfoes
dc.creatorGómez Fernández, Juan Franciscoes
dc.creatorMartínez-Galan Fernández, Pabloes
dc.creatorGuillén López, Antonio Jesúses
dc.date.accessioned2021-04-19T11:45:00Z
dc.date.available2021-04-19T11:45:00Z
dc.date.issued2020
dc.identifier.citationCrespo Márquez, A., Gómez Fernández, J.F., Martínez-Galan Fernández, P. y Guillén López, A.J. (2020). Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models. Energies, 13
dc.identifier.issn1996-1073es
dc.identifier.urihttps://hdl.handle.net/11441/107312
dc.description.abstractMaintenance Management is a key pillar in companies, especially energy utilities, which have high investments in assets, and so for its proper contribution has to be integrated and aligned with other departments in order to conserve the asset value and guarantee the services. In this line, Intelligent Assets Management Platforms (IAMP) are defined as software platforms to collect and analyze data from industrial assets. They are based on the use of digital technologies in industry. Beside the fact that monitoring and managing assets over the internet is gaining ground, this paper states that the IAMPs should also support a much better balanced and more strategic view in existing asset management and concretely in maintenance management. The real transformation can be achieved if these platforms help to understand business priorities in work and investments. In this paper, we first discuss about the factors explaining IAMP growth, then we explain the importance of considering, well in advance, those managerial aspects of the problem, for proper investments and suitable digital transformation through the adoption and use of IAMPs. A case study in the energy sector is presented to map, or to identify, those platform modules and Apps providing important value-added features to existing asset management practices. Later, attention is paid to the methodology used to develop the Apps’ data models from a maintenance point of view. To illustrate this point, a methodology for the development of the asset criticality analysis process data model is proposed. Finally, the paper includes conclusions of the work and relevant literature to this researches
dc.formatapplication/pdfes
dc.format.extent19 p.es
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofEnergies, 13
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectintelligent assets management systemses
dc.subjectindustrial IoTes
dc.subjectpredictive analyticses
dc.subjectasset data modeles
dc.titleMaintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Modelses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas Ies
dc.relation.publisherversionhttps://www.mdpi.com/1996-1073/13/15/3762es
dc.identifier.doi10.3390/en13153762es
dc.contributor.groupUniversidad de Sevilla. CEI-19-TEP134: Metodología para aplicación industrial de soluciones de mantenimiento inteligente. Integración de técnicas de Analítica Predictiva y Machine Learning en plataformas IoTes
dc.journaltitleEnergieses
dc.publication.volumen13es

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