Validación de modelos predictivos de analítica de datos de análisis de aceite usado para la toma de decisiones de mantenimiento en un turbogenerador de vapor.
ilustraciones, diagramas, mapas, tablas
- Autores:
-
Sierra Mejia, Juan Pablo
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81094
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Oil reclamation
Recuperación de aceites usados
Análisis de aceite usado
Turbogenerador de vapor
Analítica de datos
Mantenimiento predictivo
Machine Learning
Used Oil Analysis
Data Analytics
Predictive Maintenance
Steam Turbogenerator
- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional
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|
dc.title.spa.fl_str_mv |
Validación de modelos predictivos de analítica de datos de análisis de aceite usado para la toma de decisiones de mantenimiento en un turbogenerador de vapor. |
dc.title.translated.eng.fl_str_mv |
Validation of predictive models on used oil analysis data for maintenance decision making in a steam turbo generator. |
title |
Validación de modelos predictivos de analítica de datos de análisis de aceite usado para la toma de decisiones de mantenimiento en un turbogenerador de vapor. |
spellingShingle |
Validación de modelos predictivos de analítica de datos de análisis de aceite usado para la toma de decisiones de mantenimiento en un turbogenerador de vapor. 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Oil reclamation Recuperación de aceites usados Análisis de aceite usado Turbogenerador de vapor Analítica de datos Mantenimiento predictivo Machine Learning Used Oil Analysis Data Analytics Predictive Maintenance Steam Turbogenerator |
title_short |
Validación de modelos predictivos de analítica de datos de análisis de aceite usado para la toma de decisiones de mantenimiento en un turbogenerador de vapor. |
title_full |
Validación de modelos predictivos de analítica de datos de análisis de aceite usado para la toma de decisiones de mantenimiento en un turbogenerador de vapor. |
title_fullStr |
Validación de modelos predictivos de analítica de datos de análisis de aceite usado para la toma de decisiones de mantenimiento en un turbogenerador de vapor. |
title_full_unstemmed |
Validación de modelos predictivos de analítica de datos de análisis de aceite usado para la toma de decisiones de mantenimiento en un turbogenerador de vapor. |
title_sort |
Validación de modelos predictivos de analítica de datos de análisis de aceite usado para la toma de decisiones de mantenimiento en un turbogenerador de vapor. |
dc.creator.fl_str_mv |
Sierra Mejia, Juan Pablo |
dc.contributor.advisor.none.fl_str_mv |
Guevara Carazas, Fernando Jesús |
dc.contributor.author.none.fl_str_mv |
Sierra Mejia, Juan Pablo |
dc.contributor.researchgroup.spa.fl_str_mv |
Gestión, Operación y Mantenimiento de Activos - Gomac |
dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Oil reclamation Recuperación de aceites usados Análisis de aceite usado Turbogenerador de vapor Analítica de datos Mantenimiento predictivo Machine Learning Used Oil Analysis Data Analytics Predictive Maintenance Steam Turbogenerator |
dc.subject.lemb.none.fl_str_mv |
Oil reclamation Recuperación de aceites usados |
dc.subject.proposal.spa.fl_str_mv |
Análisis de aceite usado Turbogenerador de vapor Analítica de datos Mantenimiento predictivo |
dc.subject.proposal.eng.fl_str_mv |
Machine Learning Used Oil Analysis Data Analytics Predictive Maintenance |
dc.subject.proposal.fra.fl_str_mv |
Steam Turbogenerator |
description |
ilustraciones, diagramas, mapas, tablas |
publishDate |
2021 |
dc.date.issued.none.fl_str_mv |
2021-09-16 |
dc.date.accessioned.none.fl_str_mv |
2022-03-01T16:23:20Z |
dc.date.available.none.fl_str_mv |
2022-03-01T16:23:20Z |
dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
status_str |
acceptedVersion |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/81094 |
dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
url |
https://repositorio.unal.edu.co/handle/unal/81094 https://repositorio.unal.edu.co/ |
identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.references.spa.fl_str_mv |
Ahmad, R., & Kamaruddin, S. (2012). An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering, 63(1), 135–149. https://doi.org/10.1016/j.cie.2012.02.002 Albarracín, P. R. (2015). Tribología y Lubricación (T. Ingeniería, Ed.). Medellín. AXA Risk Consulting. (2020). Steam Turbine Lubricating Oil Systems. 1–4. Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 114598. https://doi.org/10.1016/j.eswa.2021.114598 Bahari, A. (2017). Investigation into Tribological Performance of Vegetable Oils as Biolubricants at Severe Contact Conditions. (October), 292. Banaszkiewicz, M. (2014). Steam turbines start-ups. Transactions of the Institute of Fluid-Flow Machinery, 126(126), 169–198. Barrios, R. (2015). 3 Medidas De Tendencia Central Y De Dispersión. Slideshare, 59. Retrieved from https://es.slideshare.net/rbarriosm/3-medidas-de-tendencia-central-y-de-dispersion-49942466 Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–213. https://doi.org/10.1016/j.ins.2011.12.028 Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics and Data Analysis, 120, 70–83. https://doi.org/10.1016/j.csda.2017.11.003 Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). A Proactive Event-driven Decision Model for Joint Equipment Predictive Maintenance and Spare Parts Inventory Optimization. Procedia CIRP, 59(TESConf 2016), 184–189. https://doi.org/10.1016/j.procir.2016.09.015 Brnabic, A., & Hess, L. M. (2021). Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making. BMC Medical Informatics and Decision Making, 21(1), 54. https://doi.org/10.1186/s12911-021-01403-2 Brown, P., & Sondalini, M. (n.d.). Asset Maintenance Management - The Path toward Defect Elimination. 1–10. Retrieved from www.lifetime-reliability.com Capuano, G., & Rimoli, J. J. (2019). Smart finite elements: A novel machine learning application. Computer Methods in Applied Mechanics and Engineering, 345, 363–381. https://doi.org/https://doi.org/10.1016/j.cma.2018.10.046 Cave, A. (2017). What Will We Do When The World’s Data Hits 163 Zettabytes In 2025? Retrieved March 8, 2019, from Forbes website: https://www.forbes.com/sites/andrewcave/2017/04/13/what-will-we-do-when-the-worlds-data-hits-163-zettabytes-in-2025/#694cc76c349a Chiu, S., & Tavella, D. (2008). Introduction to Data Mining. Data Mining and Market Intelligence for Optimal Marketing Returns, 137–192. https://doi.org/10.1016/b978-0-7506-8234-3.00007-1 Coleman, W. (1981). Water Contamination of Steam Turbine Lube Oils - How to Avoid It. Journal of Chemical Information and Modeling, 53(9), 1689–1699. Deloitte Brazil. (2021). Strategic asset management. Retrieved April 30, 2021, from https://www2.deloitte.com/br/en/pages/finance/solutions/gestao-estrategica-ativos.html Elshawi, R., Sakr, S., Talia, D., & Trunfio, P. (2018). Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service. Big Data Research, 14, 1–11. https://doi.org/10.1016/j.bdr.2018.04.004 Espino Timón, C., & Martínez Fontes, X. (2017). “Análisis predictivo: técnicas y modelos utilizados y aplicaciones del mismo - herramientas Open Source que permiten su uso. 26/27, I(Principio activo y prestación ortoprotésica), 67. Retrieved from http://openaccess.uoc.edu/webapps/o2/bitstream/10609/59565/6/caresptimTFG0117memòria.pdf Exposito, C. (2020). Clustering jerarquico. Universidad de La Laguna. Exxon Mobil. (2009). Turbine Oil System Care & Maintenance. Exxon Mobil. (2020). Mobil SHC 825. Retrieved April 2, 2021, from https://www.mobil.com.mx/es-mx/lubricantes/industrial/lubricants/products/products/mobil-shc-825 Faraldo, P. (2013). Estadística y metodología de la investigación. Universidad Santiago De Compostela, 15. Retrieved from http://eio.usc.es/eipc1/BASE/BASEMASTER/FORMULARIOS-PHP-DPTO/MATERIALES/Mat_G2021103104_EstadisticaTema1.pdf Fernando, J., & Walters, T. (2021, February). Correlation Coefficient Definition. Retrieved May 1, 2021, from https://www.investopedia.com/terms/c/correlationcoefficient.asp Fortune Business Insight. (2020). Lubricants Market Size, Share, Report. 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Reliability Engineering & System Safety, 210, 107560. https://doi.org/10.1016/j.ress.2021.107560 Hanke, J. E., & Wichern, D. W. (2010). Pronósticos en los negocios. In 9 Edición (Ed.), ممممم ممممم (Vol. 4). Retrieved from http://marefateadyan.nashriyat.ir/node/150 Hausermann, A. (1961). Particular Problems of Steam Turbine Lubrication. (Cv), 125–132. Hejnowicz, Z., Burian, A., Dobrowolska, I., & Kolano, E. (2006). Orientational variability of parallel arrays of cortical microtubules under the outer cell wall of the Helianthus hypocotyl epidermis. Acta Societatis Botanicorum Poloniae, 75(3), 201–206. https://doi.org/10.5586/asbp.2006.023 Jiang, G., & Wang, W. (2017). Markov cross-validation for time series model evaluations. Information Sciences, 375, 219–233. https://doi.org/10.1016/j.ins.2016.09.061 Jiménez Rodríguez, C., & Arias Aguilar, D. (2004). Distribución de la biomasa y densidad de raíces finas en una gradiente sucesional de bosques en la Zona Norte de Costa Rica. Revista Forestal Mesoamerica Kurú, 1(2), pág. 44-63. Joseph Omosanya, A., Titilayo Akinlabi, E., & Olusegun Okeniyi, J. (2019). Overview for Improving Steam Turbine Power Generation Efficiency. Journal of Physics: Conference Series, 1378(3). https://doi.org/10.1088/1742-6596/1378/3/032040 Lahura, E. (2003). El Coeficiente De Correlación Y Correlaciones Espúreas. Universidad Catolica Del Perú, 1–64. Lazovic, T., & Marinkovic, A. (2015). A case study of turbogenerator journal bearing failure. (February). Luo, J. (2013). Thin Film Lubrication. In Encyclopedia of Tribology (pp. 3663–3667). https://doi.org/10.1007/978-0-387-92897-5_682 Macián, V., Tormos, B., Ruíz, S., & Ramírez, L. (2015). Potential of low viscosity oils to reduce CO2 emissions and fuel consumption of urban buses fleets. Transportation Research Part D: Transport and Environment, 39, 76–88. https://doi.org/10.1016/j.trd.2015.06.006 Martha deSouza, G. F. (2012). Fundamentals of Maintenance. In Springer (Ed.), Thermal Power Plant Performance Analysis (pp. 123–146). Sao Paulo. Maxell, D. (1996). The History of the Steam Turbine. Pacific Turbines, (1629). McCoy, J. T., & Auret, L. (2019). Machine learning applications in minerals processing: A review. Minerals Engineering, 132, 95–109. https://doi.org/https://doi.org/10.1016/j.mineng.2018.12.004 Michalke, B., & Nischwitz, V. (2017). Speciation and element-specific detection. In Liquid Chromatography (pp. 753–767). https://doi.org/10.1016/B978-0-12-805392-8.00023-2 Montoya-Restrepo, N. E., & Correa-Morales, J. C. (2009). Estadístico de Procesos en el Monitoreo de la Mortalidad Perinatal. Revista de Salud Publica, 11(1), 92–99. Neale, M. J. (1973). The Tribology Handbook. In Notes and Queries (Vol. s8-VI). https://doi.org/10.1093/nq/s8-VI.151.385-b Nicholson, K. F., Richardson, R. T., van Roden, E. A. R., Quinton, R. G., Anzilotti, K. F., & Richards, J. G. (2019). Machine learning algorithms for predicting scapular kinematics. Medical Engineering & Physics, 65, 39–45. https://doi.org/https://doi.org/10.1016/j.medengphy.2019.01.005 Palladino, A. C. (2011). Gráfico de caja. Atención Primaria de Salud, Epidemiología e Informatica II, 7–10. Patiño-Rodriguez, C. E., & Guevara Carazas, F. J. (2020). Maintenance and Asset Life Cycle for Reliability Systems. Reliability and Maintenance - An Overview of Cases. https://doi.org/10.5772/intechopen.85845 Phillips 66. (2019). Next-Generation Turbine Oils Combat Oxidation , Thermal Degradation and Varnish. Pintelon, L., & Parodi-Herz, A. (2008). Maintenance: An Evolutionary Perspective. Springer Series in Reliability Engineering, 8, 21–48. https://doi.org/10.1007/978-1-84800-011-7_2 Pourahmadi, M. (2002). A Course in Time Series Analysis. The American Statistician, 56(1), 77–77. https://doi.org/10.1198/tas.2002.s131 R: The R Project for Statistical Computing. (n.d.). Retrieved April 15, 2021, from https://www.r-project.org/ Raadnui, S., & Kleesuwan, S. (2005). Low-cost condition monitoring sensor for used oil analysis. Wear, 259(7–12), 1502–1506. https://doi.org/10.1016/j.wear.2004.11.009 Raposo, H., Farinha, J. T., Fonseca, I., & Galar, D. (2019). Predicting condition based on oil analysis – A case study. Tribology International, 135(January), 65–74. https://doi.org/10.1016/j.triboint.2019.01.041 Reddy, a S., Ahmed, I., Kumar, T. S., Reddy, a V. K., & Bharathi, V. V. P. (2014). Analysis Of Steam Turbines. International Refereed Journal of Engineering and Science, 3(2), 32–48. Sander, J. (2012). Steam Turbine Oil Challenges. LE White Paper, 1–10. Scientific Spectro. (2000). Guide to Measuring TAN and TBN in Oil. Spectro Scientific, Spectro Sci. Retrieved from https://www.spectrosci.com/resource-center/lubrication-analysis/literature/e-guides/guide-to-measuring-tantbn/ Scopus. (2021a). Analyze search results for “lubricant” and “analytics.” Retrieved April 30, 2021, from https://www.scopus.com/term/analyzer.uri?sid=a091b7a8e94702c690c7bc7598eeaf06&origin=resultslist&src=s&s=TITLE-ABS-KEY%28%22lubricant%22+and+%22analytics%22%29&sort=plf-f&sdt=b&sot=b&sl=42&count=26&analyzeResults=Analyze+results&txGid=417060f79e9e194be4ae35a560a96415 Scopus. (2021b). Analyze search results for “Machine Learning” and “maintenance.” Retrieved April 30, 2021, from https://www.scopus.com/term/analyzer.uri?sid=de8fd338098c34bee445c5b4839b29cd&origin=resultslist&src=s&s=TITLE-ABS-KEY%28%22Machine+Learning%22+and+%22maintenance%22%29&sort=plf-f&sdt=b&sot=b&sl=51&count=3416&analyzeResults=Analyze+results&txGid=ed806beff Scopus - Analyze search results. (n.d.). Retrieved March 19, 2019, from https://www-scopus-com.ezproxy.unal.edu.co/term/analyzer.uri?sid=6bce59022a605e33370d56242a5ba5fe&origin=resultslist&src=s&s=TITLE-ABS-KEY%28machine+learning%29&sort=plf-f&sdt=b&sot=b&sl=31&count=171267&analyzeResults=Analyze+results&txGid=da2bb524285aa00 Shahbazi, N., Bortoluzzi, B., Raghubar, C., An, A., Fok, R., & McArthur, J. J. (2018). Machine learning and BIM visualization for maintenance issue classification and enhanced data collection. Advanced Engineering Informatics, 38(October 2017), 101–112. https://doi.org/10.1016/j.aei.2018.06.007 Shimadzu. (2003). Elemental Analysis of Additives in Lubricant Oils Using ICPE-9820. Application News, J111. Sibata. (2020). Viscosity Measurement Series. Sibata. Retrieved from https://www.sibata.co.jp/wpcms/wp-content/themes/sibata/en/pdf/viscosity_measurement_series.pdf Spakovszky, Z. (2007). Enhancements of Rankine Cycles. Retrieved March 28, 2021, from Unified: Thermodynamics and propulsion website: https://web.mit.edu/16.unified/www/FALL/thermodynamics/notes/node66.html Spectro Scientific. (2015). Guide to Measuring Water in Oil. Spectro Scientific, 5. Retrieved from https://www.spectrosci.com/resource-center/lubrication-analysis/literature/whitepapers/guide-to-measuring-water-in-oil/%5Cnhttp://www.spectrosci.com/resource-center/lubrication-analysis/literature/e-guides/guide-to-measuring-water-in-oil/ Syan, C. S., Ramsoobag, G., Mahabir, K., & Rajnauth, V. (2020). A Case Study for Improving Maintenance Planning of Centrifugal Pumps Using Condition-Based Maintenance. 42(2), 17–24. Troyer, D., & Fitch, J. (2004). Oil Analysis Basics. León: Noria. Ucci, D., Aniello, L., & Baldoni, R. (2019). Survey of machine learning techniques for malware analysis. Computers & Security, 81, 123–147. https://doi.org/https://doi.org/10.1016/j.cose.2018.11.001 Vališ, D., Žák, L., & Pokora, O. (2015). Failure prediction of diesel engine based on occurrence of selected wear particles in oil. Engineering Failure Analysis, 56, 501–511. https://doi.org/10.1016/j.engfailanal.2014.11.020 Vellido, A., Martín-Guerrero, J. D., & Lisboa, P. J. G. (2012). Making machine learning models interpretable. ESANN 2012 Proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (April), 163–172. Zhang, Z., & Sejdić, E. (2019). Radiological images and machine learning: Trends, perspectives, and prospects. Computers in Biology and Medicine. https://doi.org/https://doi.org/10.1016/j.compbiomed.2019.02.017 Zhao, Y. (2017). The Importance of Lubricant and Fluid Analysis in Predictive Maintenance. Spectro Scientific, (Figure 1), 1–6. Retrieved from https://www.spectrosci.com/blog/the-importance-of-lubricant-and-fluid-analysis-in-predictive-maintenance/ |
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http://purl.org/coar/access_right/c_abf2 |
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Atribución-NoComercial-CompartirIgual 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
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Atribución-NoComercial-CompartirIgual 4.0 Internacional http://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
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XII, 94 páginas |
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application/pdf |
dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
dc.publisher.program.spa.fl_str_mv |
Medellín - Minas - Maestría en Ingeniería Mecánica |
dc.publisher.department.spa.fl_str_mv |
Departamento de Ingeniería Mecánica |
dc.publisher.faculty.spa.fl_str_mv |
Facultad de Minas |
dc.publisher.place.spa.fl_str_mv |
Medellín, Colombia |
dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Medellín |
institution |
Universidad Nacional de Colombia |
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Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Guevara Carazas, Fernando Jesúse547a5abc87fbf8110af5d3714c280f6600Sierra Mejia, Juan Pablof0c00fc667c2504a91dbbb8dd89c9de4Gestión, Operación y Mantenimiento de Activos - Gomac2022-03-01T16:23:20Z2022-03-01T16:23:20Z2021-09-16https://repositorio.unal.edu.co/handle/unal/81094Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, mapas, tablasEn el presente trabajo se desarrollan modelos descriptivos, clasificatorios y predictivos de la analítica de datos, con el fin de generar una herramienta de toma de decisiones basadas en las observaciones capturadas de diferentes pruebas realizadas al aceite usado de un turbogenerador de vapor marca Siemens de una industria papelera. Se estructura una base de datos con la información recopilada en un periodo de seis años (81 registros).; allí se cuenta con mediciones de diferentes propiedades del lubricante, por lo que se seleccionan 4 variables principales para el análisis. Las variables seleccionadas son el Número acido total (TAN), el porcentaje de agua disuelta en el aceite, la concentración de fósforo en el aceite y la viscosidad a 40°c. Se implementan modelos de clusterización jerárquica, series de tiempo, aproximación por medias móviles y cartas de control. Por último, se presentan las conclusiones derivadas de la implementación de dichos modelos. (Texto tomado de la fuente)In this study, Data analytic models (descriptive, classificatory and predictive) are developed, in order to generate a decision-making tool based on observations obtained from different tests carried out on used oil of a Siemens brand steam turbogenerator from paper industry. A database is structured with information collected over a period of six years (81 records). There are measurements of different properties of lubricant, Then, 4 main variables are selected for analysis. Selected variables are Total Acid Number (TAN), percentage of water dissolved in oil, phosphorus concentration in oil and viscosity at 40 ° C. Hierarchical clustering models, time series, moving average approximation and control charts are implemented. Finally, Conclusions derived from the implementation of these models are presented.MaestríaMagíster en Ingeniería MecánicaMachine Learning en gestión de mantenimientoÁrea Curricular de Ingeniería MecánicaXII, 94 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería MecánicaDepartamento de Ingeniería MecánicaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaOil reclamationRecuperación de aceites usadosAnálisis de aceite usadoTurbogenerador de vaporAnalítica de datosMantenimiento predictivoMachine LearningUsed Oil AnalysisData AnalyticsPredictive MaintenanceSteam TurbogeneratorValidación de modelos predictivos de analítica de datos de análisis de aceite usado para la toma de decisiones de mantenimiento en un turbogenerador de vapor.Validation of predictive models on used oil analysis data for maintenance decision making in a steam turbo generator.Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAhmad, R., & Kamaruddin, S. 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Retrieved from https://www.spectrosci.com/blog/the-importance-of-lubricant-and-fluid-analysis-in-predictive-maintenance/EstudiantesInvestigadoresORIGINAL1037633177. 2021.pdf1037633177. 2021.pdfTesis de Maestría en Ingeniería Mecánicaapplication/pdf2235830https://repositorio.unal.edu.co/bitstream/unal/81094/4/1037633177.%202021.pdfbcc071c862ba50cc854993b918731a9dMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81094/3/license.txt8153f7789df02f0a4c9e079953658ab2MD53THUMBNAIL1037633177. 2021.pdf.jpg1037633177. 2021.pdf.jpgGenerated Thumbnailimage/jpeg5471https://repositorio.unal.edu.co/bitstream/unal/81094/5/1037633177.%202021.pdf.jpg58e3e5ecbd26c5d07dc00a4cf7c859b6MD55unal/81094oai:repositorio.unal.edu.co:unal/810942023-08-09 10:45:28.43Repositorio Institucional Universidad Nacional de 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