Optimal maintenance thresholds to perform preventive actions by using multi-objective evolutionary algorithms

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dc.contributor.authorGoti Elordi, Aitor
dc.contributor.authorOyarbide Zubillaga, Aitor
dc.contributor.authorAlberdi Celaya, Elisabete
dc.contributor.authorSánchez, Ana
dc.contributor.authorGarcía Bringas, Pablo
dc.date.accessioned2025-10-14T18:49:27Z
dc.date.available2025-10-14T18:49:27Z
dc.date.issued2019-08-01
dc.date.updated2025-10-14T18:49:27Z
dc.description.abstractMaintenance 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.en
dc.description.sponsorshipThis research was funded by the HAZITEK call of the Basque Government, project acronym HORDAGOen
dc.identifier.citationGoti, A., Oyarbide-Zubillaga, A., Alberdi, E., Sanchez, A., & Garcia-Bringas, P. (2019). Optimal maintenance thresholds to perform preventive actions by using multi-objective evolutionary algorithms. Applied Sciences (Switzerland), 9(15). https://doi.org/10.3390/APP9153068
dc.identifier.doi10.3390/APP9153068
dc.identifier.eissn2076-3417
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3967
dc.language.isoeng
dc.publisherMDPI AG
dc.rights©2019 by the authors
dc.subject.otherCondition-based maintenance
dc.subject.otherOptimization
dc.subject.otherMulti-objective evolutionary algorithms
dc.subject.otherProduction systems
dc.titleOptimal maintenance thresholds to perform preventive actions by using multi-objective evolutionary algorithmsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue15
oaire.citation.titleApplied Sciences (Switzerland)
oaire.citation.volume9
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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