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Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms

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Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms

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dc.contributor.author Goti, Aitor es_ES
dc.contributor.author Oyarbide-Zubillaga, Aitor es_ES
dc.contributor.author Alberdi, Elisabete es_ES
dc.contributor.author Sánchez Galdón, Ana Isabel es_ES
dc.contributor.author Garcia-Bringas, Pablo es_ES
dc.date.accessioned 2024-04-12T18:04:28Z
dc.date.available 2024-04-12T18:04:28Z
dc.date.issued 2019-08-01 es_ES
dc.identifier.uri http://hdl.handle.net/10251/203457
dc.description.abstract [EN] Maintenance has always been a key activity in the manufacturing industry because of its economic consequences. Nowadays, its importance is increasing thanks to the Industry 4.0 or fourth industrial revolution. There are more and more complex systems to maintain, and maintenance management must gain efficiency and effectiveness in order to keep all these devices in proper conditions. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, even though often these programs are complex to manage and understand. For this reason, several research papers propose approaches that are as simple as possible and can be understood by users and modified by experts. In this context, this paper focuses on CBM optimization in an industrial environment, with the objective of determining the optimal values of preventive intervention limits for equipment under corrective and preventive maintenance cost criteria. In this work, a cost-benefit mathematical model is developed. It considers the evolution in quality and production speed, along with condition based, corrective and preventive maintenance. The cost-benefit optimization is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case. es_ES
dc.description.sponsorship This research was funded by the HAZITEK call of the Basque Government, project acronym HORDAGO. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation info:eu-repo/grantAgreement/Eusko Jaurlaritza/HAZITEK/ZE-2019%2F00008/ES/HORNOS ROBUSTOS Y EFICIENTES CON DISPONIBILIDAD Y MANTENIMIENTO AVANZADOS PARA LA GESTIÓN OPTIMIZADA DE PROCESOS
dc.relation.ispartof Applied Sciences es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Condition-based maintenance es_ES
dc.subject Optimization es_ES
dc.subject Multi-objective evolutionary algorithms es_ES
dc.subject Production systems es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/app9153068 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.description.bibliographicCitation Goti, A.; Oyarbide-Zubillaga, A.; Alberdi, E.; Sánchez Galdón, AI.; Garcia-Bringas, P. (2019). Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms. Applied Sciences. 9(15). https://doi.org/10.3390/app9153068 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/app9153068 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 15 es_ES
dc.identifier.eissn 2076-3417 es_ES
dc.relation.pasarela S\407526 es_ES
dc.contributor.funder Eusko Jaurlaritza es_ES


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