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

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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. 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Retrieved April 30, 2021, from Market Research Report website: https://www.fortunebusinessinsights.com/industry-reports/lubricants-market-101771 Guevara Carazas, F. J., & Martha daSouza, G. F. (2010). Risk-based decision making method for maintenance policy selection of thermal power plant equipment. Energy, 35(2), 964–975. https://doi.org/10.1016/j.energy.2009.06.054 Gupta, G., & Mishra, R. P. (2016). A SWOT analysis of reliability centered maintenance framework. Journal of Quality in Maintenance Engineering, 22(2), 130–145. https://doi.org/10.1108/JQME-01-2015-0002 Gutierrez Pulido, H., & De la Vara Salazar, R. (2009). Control Estadístico de la Calidad y Seis Sigma (M. Hill, Ed.). Guanajuato. Han, X., Wang, Z., Xie, M., He, Y., Li, Y., & Wang, W. (2021). Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence. 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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.). 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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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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-CompartirIgual 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-CompartirIgual 4.0 Internacional http://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2
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dc.format.extent.spa.fl_str_mv	XII, 94 páginas
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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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spelling	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. (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.2012.02.002Albarracín, 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.2021.114598Bahari, 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. 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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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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

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.

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