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Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure

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dc.contributor.authorPrimera, Ernesto
dc.contributor.authorCacereño, Andrés
dc.contributor.authorFernández Bermejo, Daniel
dc.contributor.authorRodríguez Prieto, Álvaro
dc.date.accessioned2024-06-11T15:15:16Z
dc.date.available2024-06-11T15:15:16Z
dc.date.issued2024-06-17
dc.description.abstractRoller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.3390/machines12010069
dc.identifier.issn2075-1702
dc.identifier.urihttps://hdl.handle.net/20.500.14468/22367
dc.journal.issue1
dc.journal.titleMachines
dc.journal.volume12
dc.language.isoen
dc.publisherMDPI
dc.relation.centerE.T.S. de Ingenieros Industriales
dc.relation.departmentIngeniería de Construcción y Fabricación
dc.rightsAtribución 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.subject.keywordsbearing failure
dc.subject.keywordsprognostics
dc.subject.keywordsdata analytics
dc.subject.keywordsstatistical modeling
dc.subject.keywordspredictive maintenance
dc.titlePredictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failurees
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication35a15658-04bc-4665-9a1a-caa49a4d0ffa
relation.isAuthorOfPublicationebbef81e-9b79-4d38-ac0b-2069afa400b8
relation.isAuthorOfPublication.latestForDiscovery35a15658-04bc-4665-9a1a-caa49a4d0ffa
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