Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning.

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dc.contributor.advisorBarrera Gomez, Marien Rocio
dc.contributor.advisorAlfonso Diaz, Andres Leonardo
dc.contributor.authorQuiroga Niño, Jose Andres
dc.contributor.corporatenameUniversidad Santo Tomasspa
dc.date.accessioned2023-07-13T14:53:40Z
dc.date.available2023-07-13T14:53:40Z
dc.date.issued2023-06-27
dc.descriptionDesarrollo una plataforma que capture y analice información según algoritmos de Aprendizaje automático, proveniente de parámetros operacionales y rutinas de mantenimiento de sistemas industriales de aire acondicionado. Prediciendo la aparición de fugas de gas refrigerante, mediante el análisis de desviaciones operacionales.spa
dc.description.abstractDevelopment of a platform that captures and analyzes information according to machine learning algorithms, from operational parameters and maintenance routines of industrial air conditioning systems. Predicting the occurrence of refrigerant gas leaks, by analyzing operational deviations.spa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagister en Ingenieríaspa
dc.format.mimetypeapplication/pdf
dc.identifier.citationAlfonso Diaz, A. L., Barrera Gomez, M. R., & Quiroga Niño, J. A. (2023). Aplicacion de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning. Universidad Santo Tomas.spa
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/11634/51260
dc.language.isospa
dc.publisherUniversidad Santo Tomásspa
dc.publisher.branchCRAI-USTA Tunjaspa
dc.publisher.facultyFacultad de Ingeniería Electrónicaspa
dc.publisher.programMaestría Ingenieríaspa
dc.relation.referencesAamir, M., & Khan, M. N. A. (2017). Incorporating quality control activities in scrum in relation to the concept of test backlog. Sadhana - Academy Proceedings in Engineering Sciences, 42(7), 1051–1061. https://doi.org/10.1007/s12046-017-0688-7spa
dc.relation.referencesAlfonso Diaz, A. L., Barrera Gomez, M. R., & Quiroga Niño, J. A. (2022). PREDICONFORT 1.0 (No. 1). Universidad Santo Tomas.spa
dc.relation.referencesAllison, P. (2013). What’s the best R-squared for logistic regression. Statistical Horizons, 13. https://statisticalhorizons.com/r2logistic/spa
dc.relation.referencesAl-Tal, M., Al-Aomar, R., & Abel, J. (2021). A predictive model for an effective maintenance of hospital critical systems. Proceedings of the 33rd European Modeling & Simulation Symposium, 1–8. https://doi.org/10.46354/i3m.2021.emss.001spa
dc.relation.referencesAmerican Society of Heating, R. and A.-C. E. (2021). ASHRAE Handbook: Vol. Fundamentals.spa
dc.relation.referencesANSI/ASHRAE 55 - 2020. Thermal Environmental Conditions for Human Occupancy, ASHRAE (2020).spa
dc.relation.referencesBartodziej, C. J. (2017). The Concept Industry 4.0. An Empirical Analysis of Technologies and Applications in Production Logistics. Springer Gabler.spa
dc.relation.referencesBoero, C. (2020). Mantenimiento industrial. Jorge Sarmiento Editor - Universitas. https://elibro.net/es/lc/usta/titulos/172523spa
dc.relation.referencesBouabdallaoui, Y., Lafhaj, Z., Yim, P., Ducoulombier, L., & Bennadji, B. (2021). Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach. Sensors, 21(4), 1044. https://doi.org/10.3390/s21041044spa
dc.relation.referencesBraun, J., Claridge, D. E., Katipaluma, S., Liu, M., & Pratt, R. G. (2001). Operation and Maintenance. In J. F. Kreider (Ed.), Handbook of heating, Ventilation, and Air Conditioning. CRC Press LLC.spa
dc.relation.referencesCandanedo, I. S., Nieves, E. H., González, S. R., Martín, M. T. S., & Briones, A. G. (2018). Machine Learning Predictive Model for Industry 4.0. In L. Uden, B. Hadzima, & I.-H. Ting (Eds.), Knowledge Management in Organizations (pp. 501–510). Springer International Publishing.spa
dc.relation.referencesCerquitelli, T., Nikolakis, N., O’Mahony, N., Macii, E., Ippolito, M., & Makris, S. (Eds.). (2021). Predictive Maintenance in Smart Factories. Springer Singapore. https://doi.org/10.1007/978-981-16-2940-2spa
dc.relation.referencesChai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE). Geoscientific Model Development Discussions, 7(1), 1525–1534.spa
dc.relation.referencesDanfoss A/S. (2017). Datasheet. Pressure Switch and Thermostat. In AI000086439001en-001001 (pp. 1–20).spa
dc.relation.referencesDas, K., Jiang, J., & Rao, J. N. K. (2004). Mean squared error of empirical predictor. The Annals of Statistics, 32(2), 818–840.spa
dc.relation.referencesdel Val Román, J. L. (2016). Industria 4.0: la transformación digital de la industria. Valencia: Conferencia de Directores y Decanos de Ingeniería Informática, Informes CODDII.spa
dc.relation.referencesDiercks, P., Gläser, D., Lünsdorf, O., Selzer, M., Flemisch, B., & Unger, J. F. (2022). Evaluation of tools for describing, reproducing and reusing scientific workflows.spa
dc.relation.referencesEbert, C., Abrahamsson, P., & Oza, N. (2012). Lean software development. IEEE Software, 29(05), 22–25.spa
dc.relation.referencesEchavarria Ortiz, H. N. (2022). Aplicación de machine learning para la enseñanza – aprendizaje de competencias ciudadanas en educación media del Colegio de Boyacá. https://repository.usta.edu.co/handle/11634/47603#.Y4pGaRabz-o.mendeleyspa
dc.relation.referencesECOPETROL S.A. (2018). Especificaciones tecnicas para el servicio de mantenimiento de sistemas de aire acondicionado de la Gerencia Refineria de Barrancabermeja de ECOPETROL S.A.spa
dc.relation.referencesElallaoui, M., Nafil, K., & Touahni, R. (2018). Automatic Transformation of User Stories into UML Use Case Diagrams using NLP Techniques. Procedia Computer Science, 130, 42–49. https://doi.org/10.1016/j.procs.2018.04.010spa
dc.relation.referencesEs-sakali, N., Cherkaoui, M., Mghazli, M. O., & Naimi, Z. (2022). Review of predictive maintenance algorithms applied to HVAC systems. Energy Reports, 8, 1003–1012. https://doi.org/10.1016/j.egyr.2022.07.130spa
dc.relation.referencesEsteki, M., Javdani Gandomani, T., & Khosravi Farsani, H. (2020). A risk management framework for distributed scrum using PRINCE2 methodology. Bulletin of Electrical Engineering and Informatics, 9(3), 1299–1310. https://doi.org/10.11591/eei.v9i3.1905spa
dc.relation.referencesFernando, J. (2021, September 12). R-Squared Formula, Regression, and Interpretations. Investopedia.Com/ Corporate Finance / Financial Analysis. https://www.investopedia.com/terms/r/r-squared.asp#:~:text=R%2Dsquared%20values%20range%20from,)%20you%20are%20interested%20in).spa
dc.relation.referencesGelman, A., Goodrich, B., Gabry, J., & Vehtari, A. (2019). R-squared for Bayesian Regression Models. The American Statistician, 73(3), 307–309. https://doi.org/10.1080/00031305.2018.1549100spa
dc.relation.referencesGonzalez Ajuech, V. L. (2017). Mantenimiento: tecnicas y aplicaciones industriales. Grupo Editorial Patria. https://elibro.net/es/lc/usta/titulos/40508spa
dc.relation.referencesHeras del Dedo, R. de las, & Alvarez Garcia, A. (2017). Metodos agiles: Scrum, Kanban, Lean. Difusora Larousse - Anaya Multimedia. https://elibro.net/es/lc/usta/titulos/122933spa
dc.relation.referencesHosamo, H. H., Svennevig, P. R., Svidt, K., Han, D., & Nielsen, H. K. (2022). A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics. Energy and Buildings, 261, 111988. https://doi.org/10.1016/j.enbuild.2022.111988spa
dc.relation.referencesInstituto de Hidrología, M. y E. A.-I. (2023). Altas Climatologico. Http://Atlas.Ideam.Gov.Co/VisorAtlasClimatologico.Html.spa
dc.relation.referencesJimenez Raya, F. (2015). Mantenimiento preventivo de sistemas de automatizacion industrial. ELEM0311. IC Editorial. https://elibro.net/es/lc/usta/titulos/59239spa
dc.relation.referencesJoshi, A. V. (2020). Machine Learning and Artificial Intelligence. Springer International Publishing. https://doi.org/10.1007/978-3-030-26622-6spa
dc.relation.referencesKe, Y., Mulumba, T., Shen, W., & Afshari, A. (2014, May 18). Model-based predictive maintenance of chillers. ACRA 2014 - Proceedings of the 7th Asian Conference on Refrigeration and Air Conditioning.spa
dc.relation.referencesKubat, M. (2017). An Introduction to Machine Learning. Springer International Publishing. https://doi.org/10.1007/978-3-319-63913-0spa
dc.relation.referencesL’Esteve, R. C. (2021). The Definitive Guide to Azure Data Engineering. Apress. https://doi.org/10.1007/978-1-4842-7182-7spa
dc.relation.referencesMartínez, R., Parkinson, C., Caruso, M., López, D., Vargas, R., & Rojas, N. (2022). Propuesta de técnicas de validación para la calidad de datos abiertos e identificación de patrones para predicciones con Machine Learning. XXIV Workshop de Investigadores En Ciencias de La Computación (WICC 2022, Mendoza).spa
dc.relation.referencesMicrosoft. (2022). Documentación de ML.NET. https://learn.microsoft.com/es-es/dotnet/machine-learning/spa
dc.relation.referencesMicrosoft. (2022). Documentación de Visual Studio. https://learn.microsoft.com/es-es/visualstudio/spa
dc.relation.referencesMicrosoft. (2022). Documentacion SQL Server Management Studio (SSMS). https://learn.microsoft.com/es-es/sql/ssms/sql-server-management-studio-ssmsspa
dc.relation.referencesMonroy Mejia, M. de los A., & Nava Sanchezllanes, N. (2018). Metodologia de la investigacion. Grupo Editorial Exodo. https://elibro.net/es/lc/usta/titulos/172512spa
dc.relation.referencesOficina Asesora de Planeacion y Estudios Sectoriales. (2019). Aspectos Basicos de la Industria 4.0. Ministerio de Tecnologias de la Informacion y las Comunicaciones. Republica de Colombia.spa
dc.relation.referencesO’hEocha, C., & Conboy, K. (2010). The Role of the User Story Agile Practice in Innovation (pp. 20–30). https://doi.org/10.1007/978-3-642-16416-3_3spa
dc.relation.referencesOhta, R. (2018). SELECTION OF INDUSTRIAL MAINTENANCE STRATEGY: CLASSICAL AHP AND FUZZY AHP APPLICATIONS. International Journal of the Analytic Hierarchy Process, 10(2). https://doi.org/10.13033/ijahp.v10i2.551spa
dc.relation.referencesPanesar, A. (2021). Evaluating Machine Learning Models. In Machine Learning and AI for Healthcare (pp. 189–205). Apress. https://doi.org/10.1007/978-1-4842-6537-6_7spa
dc.relation.referencesPanfilov, P., & Katona, A. (2018). Building Predictive Maintenance Framework for Smart Environment Application Systems (pp. 0460–0470). https://doi.org/10.2507/29th.daaam.proceedings.068spa
dc.relation.referencesPeriyasamy, K., & Chianelli, J. (2021). A project tracking tool for scrum projects with machine learning support for cost estimation. EPiC Series in Computing, 76, 86–94. https://doi.org/10.29007/6vwhspa
dc.relation.referencesRodal Montero, E. (2020). Industria 4.0: conceptos, tecnologias habilitadoras y retos. Difusora Larousse - Ediciones Piramide. https://elibro.net/es/lc/usta/titulos/216140spa
dc.relation.referencesSalvaris, M., Dean, D., & Tok, W. H. (2018). Deep Learning with Azure. Apress. https://doi.org/10.1007/978-1-4842-3679-6spa
dc.relation.referencesSandoval, R. (2022). IoT: La conexión con la industria del mañana. Mundo HVAC&R.spa
dc.relation.referencesSantiago, A. R., Antunes, M., Barraca, J. P., Gomes, D., & Aguiar, R. L. (2019). Predictive Maintenance System for Efficiency Improvement of Heating Equipment. 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 93–98. https://doi.org/10.1109/BigDataService.2019.00019spa
dc.relation.referencesSantos Sanchez, G. A., & Castro Barrera, M. A. (2022). Metodologia de valoracion de riesgo de equipo electrico: evaluacion basada en el conocimiento experto. https://cimga.com/spa
dc.relation.referencesSanzana, M. R., Maul, T., Wong, J. Y., Abdulrazic, M. O. M., & Yip, C.-C. (2022). Application of deep learning in facility management and maintenance for heating, ventilation, and air conditioning. Automation in Construction, 141, 104445. https://doi.org/10.1016/J.AUTCON.2022.104445spa
dc.relation.referencesSaturno, M., Pertel, V., & Deschamps, F. (2017). Proposal of an automation solutions architecture for Industry 4.0.spa
dc.relation.referencesSedeño, J., Schön, E.-M., Torrecilla-Salinas, C., Thomaschewski, J., Escalona, M. J., & Mejias, M. (2017). Modelling agile requirements using context-based persona stories. WEBIST 2017 - Proceedings of the 13th International Conference on Web Information Systems and Technologies, 196–203. https://doi.org/10.5220/0006220301960203spa
dc.relation.referencesSIEMENS. (2023). SIEMENS MindSphere. https://new.siemens.com/co/es/productos/software/mindsphere.htmlspa
dc.relation.referencesSrinivasan, R., Tamzeed Islam, M., Islam, B., Wang, Z., Sookoor, T., Gnawali, O., & Nirjon, S. (2017, August 7). Preventive Maintenance of Centralized HVAC Systems: Use of Acoustic Sensors, Feature Extraction, and Unsupervised Learning. https://doi.org/10.26868/25222708.2017.715spa
dc.relation.referencesStanfield, C., & Skaves, D. (2013). Fundamentals of HVACR (A. Vernon R, Ed.; 2nd ed.). PEARSON.spa
dc.relation.referencesSubih, M. A., Malik, B. H., Mazhar, I., Sabir, U., Wakeel, T., Wajid, A., Yousaf, A., Nawaz, H., & Suleman, M. (2019). Comparison of agile method and scrum method with software quality affecting factors. International Journal of Advanced Computer Science and Applications, 10(5).spa
dc.relation.referencesSukhodolov, Y. A. (2019). The Notion, Essence, and Peculiarities of Industry 4.0 as a Sphere of Industry. In E. G. Popkova, Y. V Ragulina, & A. V Bogoviz (Eds.), Industry 4.0: Industrial Revolution of the 21st Century (pp. 3–10). Springer International Publishing. https://doi.org/10.1007/978-3-319-94310-7_1spa
dc.relation.referencesTaibi, D., Lenarduzzi, V., Janes, A., Liukkunen, K., & Ahmad, M. O. (2017). Comparing Requirements Decomposition Within the Scrum, Scrum with Kanban, XP, and Banana Development Processes (pp. 68–83). https://doi.org/10.1007/978-3-319-57633-6_5spa
dc.relation.referencesTrivedi, S., Bhola, S., Talegaonkar, A., Gaur, P., & Sharma, S. (2019). Predictive Maintenance of Air Conditioning Systems Using Supervised Machine Learning. 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP), 1–6. https://doi.org/10.1109/ISAP48318.2019.9065995spa
dc.relation.referencesUrbieta, M., Antonelli, L., Rossi, G., & do Prado Leite, J. C. S. (2020). The impact of using a domain language for an agile requirement management. Information and Software Technology, 127. https://doi.org/10.1016/j.infsof.2020.106375spa
dc.relation.referencesVijayaraghavan, V., & Rian Leevinson, J. (2019). Internet of Things Applications and Use Cases in the Era of Industry 4.0 (pp. 279–298). https://doi.org/10.1007/978-3-030-24892-5_12spa
dc.relation.referencesWautelet, Y., Gielis, D., Poelmans, S., & Heng, S. (2019). Evaluating the impact of user stories quality on the ability to understand and structure requirements. In Lecture Notes in Business Information Processing (Vol. 369). https://doi.org/10.1007/978-3-030-35151-9_1spa
dc.relation.referencesWEG. (2023). WEG IOT Platform. https://iot.weg.net/#/portal/homespa
dc.relation.referencesYang, C., Chen, Q., Shen, W., & Gunay, B. (2017). Toward failure mode and effect analysis for heating, ventilation and air-conditioning. 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD), 408–413. https://doi.org/10.1109/CSCWD.2017.8066729spa
dc.relation.referencesYang, C., Gunay, B., Shi, Z., & Shen, W. (2021). Machine Learning-Based Prognostics for Central Heating and Cooling Plant Equipment Health Monitoring. IEEE Transactions on Automation Science and Engineering, 18(1), 346–355. https://doi.org/10.1109/TASE.2020.2998586spa
dc.relation.referencesYang, C., Shen, W., Chen, Q., & Gunay, B. (2018). A practical solution for HVAC prognostics: Failure mode and effects analysis in building maintenance. Journal of Building Engineering, 15, 26–32. https://doi.org/10.1016/j.jobe.2017.10.013spa
dc.relation.referencesYang, C., Shen, W., Gunay, B., & Shi, Z. (2019). Toward machine learning-based prognostics for heating ventilation and air-conditioning systems. ASHRAE Transactions Volume 125, 106–115.spa
dc.relation.referencesYORK International. Jhonson Controls. (2004). Installation manual. Predator. Single package air conditioners and single package electric units DM090, 120 and 150. 7-1/2 TO 12 1/2 TO (380V, 3Phase, 60 HZ). In 035-17311-003-A-0704. Jhonson Controls.spa
dc.relation.referencesYuliansyah, H., Qudsiah, S. N., Zahrotun, L., & Arfiani, I. (2018). Implementation of use case point as software effort estimation in Scrum Framework. IOP Conference Series: Materials Science and Engineering, 403(1). https://doi.org/10.1088/1757-899X/403/1/012085spa
dc.relation.referencesZhang, J., Liu, C., & Gao, R. X. (2022). Physics-guided Gaussian process for HVAC system performance prognosis. Mechanical Systems and Signal Processing, 179, 109336. https://doi.org/10.1016/j.ymssp.2022.109336spa
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.subject.keywordPredictive maintenancespa
dc.subject.keywordIndustrial refrigerationspa
dc.subject.keywordScrum methodologyspa
dc.subject.keywordUser storiesspa
dc.subject.keywordMachine learningspa
dc.subject.keywordDesktop applicationspa
dc.subject.proposalMantenimiento predictivospa
dc.subject.proposalRefrigeracion industrialspa
dc.subject.proposalMetodología scrumspa
dc.subject.proposalHistorias de usuariospa
dc.subject.proposalAprendizaje automaticospa
dc.subject.proposalAplicación de escritoriospa
dc.titleAplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning.spa
dc.typemaster thesis
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driveinfo:eu-repo/semantics/masterThesis
dc.type.localTesis de maestríaspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersion

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