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

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...

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Autores: Goti Elordi, Aitor, Oyarbide Zubillaga, Aitor, Alberdi Celaya, Elisabete, Sánchez, Ana, García Bringas, Pablo
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/39091
Acceso en línea:http://hdl.handle.net/10810/39091
Access Level:acceso abierto
Palabra clave:condition-based maintenance
optimization
multi-objective evolutionary algorithms
production systems
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spelling Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary AlgorithmsGoti Elordi, AitorOyarbide Zubillaga, AitorAlberdi Celaya, ElisabeteSánchez, AnaGarcía Bringas, Pablocondition-based maintenanceoptimizationmulti-objective evolutionary algorithmsproduction systemsMaintenance 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.This research was funded by the HAZITEK call of the Basque Government, project acronym HORDAGO.MDPI202020202019info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/39091reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.mdpi.com/2076-3417/9/15/3068info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citedAtribución 3.0 Españaoai:addi.ehu.eus:10810/390912026-06-18T09:23:17Z
dc.title.none.fl_str_mv Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms
title Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms
spellingShingle Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms
Goti Elordi, Aitor
condition-based maintenance
optimization
multi-objective evolutionary algorithms
production systems
title_short Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms
title_full Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms
title_fullStr Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms
title_full_unstemmed Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms
title_sort Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms
dc.creator.none.fl_str_mv Goti Elordi, Aitor
Oyarbide Zubillaga, Aitor
Alberdi Celaya, Elisabete
Sánchez, Ana
García Bringas, Pablo
author Goti Elordi, Aitor
author_facet Goti Elordi, Aitor
Oyarbide Zubillaga, Aitor
Alberdi Celaya, Elisabete
Sánchez, Ana
García Bringas, Pablo
author_role author
author2 Oyarbide Zubillaga, Aitor
Alberdi Celaya, Elisabete
Sánchez, Ana
García Bringas, Pablo
author2_role author
author
author
author
dc.subject.none.fl_str_mv condition-based maintenance
optimization
multi-objective evolutionary algorithms
production systems
topic condition-based maintenance
optimization
multi-objective evolutionary algorithms
production systems
description 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.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/39091
url http://hdl.handle.net/10810/39091
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2076-3417/9/15/3068
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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