dc.contributor.editor | Beer, Michael | es |
dc.contributor.editor | Zio, Enrico | es |
dc.creator | Izquierdo, Juan | es |
dc.creator | Crespo Márquez, Adolfo | es |
dc.creator | Uribetxebarria, Jone | es |
dc.creator | Erguido, Asier | es |
dc.date.accessioned | 2020-06-12T16:18:43Z | |
dc.date.available | 2020-06-12T16:18:43Z | |
dc.date.issued | 2019-09 | |
dc.identifier.citation | Izquierdo, J., Crespo Márquez, A., Uribetxebarria, J. y Erguido, A. (2019). Comprehensive clustering approach for managing maintenance in large fleet of assets. En 29th European Safety and Reliability Conference (ESREL 2019) (515-522), Hannover, Germany: Research Publishing. | |
dc.identifier.isbn | 978-981-11-2724-3 | es |
dc.identifier.uri | https://hdl.handle.net/11441/97756 | |
dc.description | Proceedings of the 29th European Safety and Reliability Conference (ESREL), 22 – 26 September 2019, Hannover, Germany. Editors, Michael Beer and Enrico Zio | es |
dc.description.abstract | The maintenance management of large fleets of assets which include several technical solutions operating in different
operational contexts has been a recurrent research topic in the literature. Current approaches to establishing fleet
maintenance plans are primarily criticality-based, considering failures consequences and assets reliability; the
reliability model is often supported by the idea of pooling data from similar pieces of equipment. In spite of
the capability to reduce the population offered by data-pooling, its criteria may still lead to a quite large number
of segments. Therefore, it results in an equally large amount of maintenance plans along with their inherent
operational and administrative difficulties. It is the purpose of the paper to introduce a novel and comprehensive
approach; it integrates statistical methods and clustering algorithms to render a fleet segmentation which allows
better customization of maintenance plans involving fewer efforts. The approach is summarized in a decision chart
which collects the logic behind the use of every algorithm, tool and technique. | es |
dc.format | application/pdf | es |
dc.format.extent | 8 p. | es |
dc.language.iso | eng | es |
dc.publisher | Research Publishing | es |
dc.relation.ispartof | 29th European Safety and Reliability Conference (ESREL 2019) (2019), p 515-522 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Maintenance | es |
dc.subject | Fleet of assets | es |
dc.subject | Reliability | es |
dc.subject | Clustering | es |
dc.subject | Operational context | es |
dc.subject | Proportional hazards | es |
dc.title | Comprehensive clustering approach for managing maintenance in large fleet of assets | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I | es |
dc.relation.publisherversion | http://rpsonline.com.sg/proceedings/9789811127243/html/0094.xml | es |
dc.identifier.doi | 10.3850/978-981-11-2724-3_0094-cd | es |
dc.contributor.group | Universidad de Sevilla. TEP134: Organizacion Industrial | es |
dc.publication.initialPage | 515 | es |
dc.publication.endPage | 522 | es |
dc.eventtitle | 29th European Safety and Reliability Conference (ESREL 2019) | es |
dc.eventinstitution | Hannover, Germany | es |
dc.relation.publicationplace | Singapore | es |