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.contributorGuevara Carazas, Fernando Jesús
dc.contributorGestión, Operación y Mantenimiento de Activos - Gomac
dc.creatorSierra Mejia, Juan Pablo
dc.date.accessioned2022-03-01T16:23:20Z
dc.date.accessioned2022-09-21T19:39:46Z
dc.date.available2022-03-01T16:23:20Z
dc.date.available2022-09-21T19:39:46Z
dc.date.created2022-03-01T16:23:20Z
dc.date.issued2021-09-16
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/81094
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3417645
dc.description.abstractEn 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)
dc.description.abstractIn 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.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherMedellín - Minas - Maestría en Ingeniería Mecánica
dc.publisherDepartamento de Ingeniería Mecánica
dc.publisherFacultad de Minas
dc.publisherMedellín, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Medellín
dc.relationAhmad, 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
dc.relationAlbarracín, P. R. (2015). Tribología y Lubricación (T. Ingeniería, Ed.). Medellín.
dc.relationAXA Risk Consulting. (2020). Steam Turbine Lubricating Oil Systems. 1–4.
dc.relationAyvaz, 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
dc.relationBahari, A. (2017). Investigation into Tribological Performance of Vegetable Oils as Biolubricants at Severe Contact Conditions. (October), 292.
dc.relationBanaszkiewicz, M. (2014). Steam turbines start-ups. Transactions of the Institute of Fluid-Flow Machinery, 126(126), 169–198.
dc.relationBarrios, 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
dc.relationBergmeir, 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
dc.relationBergmeir, 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
dc.relationBousdekis, 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
dc.relationBrnabic, 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
dc.relationBrown, P., & Sondalini, M. (n.d.). Asset Maintenance Management - The Path toward Defect Elimination. 1–10. Retrieved from www.lifetime-reliability.com
dc.relationCapuano, 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
dc.relationCave, 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
dc.relationChiu, 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
dc.relationColeman, W. (1981). Water Contamination of Steam Turbine Lube Oils - How to Avoid It. Journal of Chemical Information and Modeling, 53(9), 1689–1699.
dc.relationDeloitte Brazil. (2021). Strategic asset management. Retrieved April 30, 2021, from https://www2.deloitte.com/br/en/pages/finance/solutions/gestao-estrategica-ativos.html
dc.relationElshawi, 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
dc.relationEspino 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
dc.relationExposito, C. (2020). Clustering jerarquico. Universidad de La Laguna.
dc.relationExxon Mobil. (2009). Turbine Oil System Care & Maintenance.
dc.relationExxon 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
dc.relationFaraldo, 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
dc.relationFernando, J., & Walters, T. (2021, February). Correlation Coefficient Definition. Retrieved May 1, 2021, from https://www.investopedia.com/terms/c/correlationcoefficient.asp
dc.relationFortune Business Insight. (2020). Lubricants Market Size, Share, Report. Retrieved April 30, 2021, from Market Research Report website: https://www.fortunebusinessinsights.com/industry-reports/lubricants-market-101771
dc.relationGuevara 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
dc.relationGupta, 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
dc.relationGutierrez Pulido, H., & De la Vara Salazar, R. (2009). Control Estadístico de la Calidad y Seis Sigma (M. Hill, Ed.). Guanajuato.
dc.relationHan, 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. Reliability Engineering & System Safety, 210, 107560. https://doi.org/10.1016/j.ress.2021.107560
dc.relationHanke, 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
dc.relationHausermann, A. (1961). Particular Problems of Steam Turbine Lubrication. (Cv), 125–132.
dc.relationHejnowicz, 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
dc.relationJiang, 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
dc.relationJiménez 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.
dc.relationJoseph 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
dc.relationLahura, E. (2003). El Coeficiente De Correlación Y Correlaciones Espúreas. Universidad Catolica Del Perú, 1–64.
dc.relationLazovic, T., & Marinkovic, A. (2015). A case study of turbogenerator journal bearing failure. (February).
dc.relationLuo, J. (2013). Thin Film Lubrication. In Encyclopedia of Tribology (pp. 3663–3667). https://doi.org/10.1007/978-0-387-92897-5_682
dc.relationMacián, 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
dc.relationMartha deSouza, G. F. (2012). Fundamentals of Maintenance. In Springer (Ed.), Thermal Power Plant Performance Analysis (pp. 123–146). Sao Paulo.
dc.relationMaxell, D. (1996). The History of the Steam Turbine. Pacific Turbines, (1629).
dc.relationMcCoy, 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
dc.relationMichalke, 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
dc.relationMontoya-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.
dc.relationNeale, M. J. (1973). The Tribology Handbook. In Notes and Queries (Vol. s8-VI). https://doi.org/10.1093/nq/s8-VI.151.385-b
dc.relationNicholson, 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
dc.relationPalladino, A. C. (2011). Gráfico de caja. Atención Primaria de Salud, Epidemiología e Informatica II, 7–10.
dc.relationPatiño-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
dc.relationPhillips 66. (2019). Next-Generation Turbine Oils Combat Oxidation , Thermal Degradation and Varnish.
dc.relationPintelon, 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
dc.relationPourahmadi, M. (2002). A Course in Time Series Analysis. The American Statistician, 56(1), 77–77. https://doi.org/10.1198/tas.2002.s131
dc.relationR: The R Project for Statistical Computing. (n.d.). Retrieved April 15, 2021, from https://www.r-project.org/
dc.relationRaadnui, 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
dc.relationRaposo, 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
dc.relationReddy, 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.
dc.relationSander, J. (2012). Steam Turbine Oil Challenges. LE White Paper, 1–10.
dc.relationScientific 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/
dc.relationScopus. (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
dc.relationScopus. (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
dc.relationScopus - 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
dc.relationShahbazi, 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
dc.relationShimadzu. (2003). Elemental Analysis of Additives in Lubricant Oils Using ICPE-9820. Application News, J111.
dc.relationSibata. (2020). Viscosity Measurement Series. Sibata. Retrieved from https://www.sibata.co.jp/wpcms/wp-content/themes/sibata/en/pdf/viscosity_measurement_series.pdf
dc.relationSpakovszky, 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
dc.relationSpectro 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/
dc.relationSyan, 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.
dc.relationTroyer, D., & Fitch, J. (2004). Oil Analysis Basics. León: Noria.
dc.relationUcci, 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
dc.relationVališ, 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
dc.relationVellido, 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.
dc.relationZhang, 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
dc.relationZhao, 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/
dc.rightsAtribución-NoComercial-CompartirIgual 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleValidación 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.typeTesis


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