Aplicación del Mantenimiento Predictivo en la Industria Petrolera: Una Revisión Exhaustiva

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dc.contributor.advisorMaldonado Moreno, Jerson Fabian
dc.contributor.authorGutiérrez Jiménez, Yekini Mateo
dc.contributor.corporatenameUniversidad Santo Tomás
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001712701
dc.contributor.googlescholarhttps://scholar.google.es/citations?hl=es&user=uRbUPY0AAAAJ&scilu=&scisig=AMD79ooAAAAAY4TC_QQaJ-vuzqcbyHlq4PrrSfuKuSSn&gmla=AJsN-F6MrzbVxRU5sjJmSAp44MFRC7hNNIktxgWtD8MYd8q6XsLGqNWOr61BiEPBA-MBsvBWsX01xFdJ6EWHM5l1YL4v8V7itEr0-6wHkrcfWRqXpEt3ZrW1tBWKORvpJcXsazcgI-27&sciund=7173997066276809121
dc.contributor.orcidhttps://orcid.org/0000-0002-4919-6150
dc.date.accessioned2025-10-14T21:25:25Z
dc.date.available2025-10-14T21:25:25Z
dc.date.issued2025-05-28
dc.descriptionEl mantenimiento predictivo ha revolucionado la industria petrolera al optimizar la gestión de activos y reducir los costos operativos mediante la anticipación de fallas en equipos críticos. Este trabajo revisa exhaustivamente las tecnologías aplicadas en el sector, destacando el uso de machine learning, redes neuronales, máquinas de soporte vectorial y el análisis de Weibull. Se analizan estudios recientes que han implementado estas técnicas para mejorar la confiabilidad de sistemas de bombeo, turbocompresores y otros equipos clave. Los resultados demuestran que el uso de algoritmos avanzados permite predecir fallos con alta precisión, reduciendo tiempos de inactividad y optimizando la toma de decisiones en mantenimiento. Finalmente, se identifican los principales desafíos en la implementación de estas tecnologías y se proponen futuras líneas de investigación para mejorar su adopción en la industria petrolera.
dc.description.abstractPredictive maintenance has revolutionized the oil industry by optimizing asset management and reducing operational costs through the anticipation of failures in critical equipment. This article provides a comprehensive review of the technologies applied in the sector, highlighting the use of machine learning, neural networks, support vector machines, and Weibull analysis. Recent studies that have implemented these techniques to enhance the reliability of pumping systems, turbo compressors, and other key equipment are analyzed. The results demonstrate that the use of advanced algorithms enables highly accurate failure prediction, reducing downtime and optimizing maintenance decision-making. Finally, the main challenges in implementing these technologies are identified, and future research directions are proposed to improve their adoption in the oil industry.
dc.description.degreelevelEspecializaciónspa
dc.description.degreenameEspecialista en Gerencia de Mantenimiento y Gestión de Activosspa
dc.description.domainhttp://www.ustavillavicencio.edu.co/home/index.php/unidades/extension-y-proyeccion/investigacion
dc.format.mimetypeapplication/pdf
dc.identifier.citationGutiérrez Jiménez, Y. (2025). Aplicación del Mantenimiento Predictivo en la Industria Petrolera: Una Revisión Exhaustiva. [Trabajo de Grado, Universidad Santo Tomás].Repositorio Institucional
dc.identifier.instnameinstname:Universidad Santo Tomásspa
dc.identifier.reponamereponame:Repositorio Institucional Universidad Santo Tomásspa
dc.identifier.repourlrepourl:https://repository.usta.edu.cospa
dc.identifier.urihttp://hdl.handle.net/11634/70105
dc.language.isospa
dc.publisherUniversidad Santo Tomásspa
dc.publisher.branchCRAI-USTA Villavicencio
dc.publisher.facultyFacultad de Ingeniería Mecánicaspa
dc.publisher.programEspecialización en Gerencia de Mantenimiento y Gestión de Activosspa
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dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Colombiaen
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.subject.keywordPredictive maintenance
dc.subject.keywordOil industry
dc.subject.keywordMachine learning
dc.subject.keywordNeural networks
dc.subject.keywordSVM
dc.subject.keywordFault diagnosis.
dc.subject.lembMantenimiento predictivo - Industria petrolera
dc.subject.lembIndustria del petróleo - Machine learning
dc.subject.lembConfiabilidad (Ingeniería) - Diagnóstico de fallas
dc.subject.lembTesis y Disertaciones académicas
dc.subject.proposalMantenimiento predictivo
dc.subject.proposalIndustria petrolera
dc.subject.proposalMachine learning
dc.subject.proposalRedes neuronales
dc.subject.proposalSVM
dc.subject.proposalDiagnóstico de fallas.
dc.titleAplicación del Mantenimiento Predictivo en la Industria Petrolera: Una Revisión Exhaustiva
dc.typebachelor thesis
dc.type.categoryFormación de Recurso Humano para la Ctel: Trabajo de grado de Especialización
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driveinfo:eu-repo/semantics/bachelorThesisspa
dc.type.localTrabajo de gradospa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa

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