Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning.
Desarrollo 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 de...
- Autores:
-
Quiroga Niño, Jose Andres
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Santo Tomás
- Repositorio:
- Repositorio Institucional USTA
- Idioma:
- spa
- OAI Identifier:
- oai:repository.usta.edu.co:11634/51260
- Acceso en línea:
- http://hdl.handle.net/11634/51260
- Palabra clave:
- Predictive maintenance
Industrial refrigeration
Scrum methodology
User stories
Machine learning
Desktop application
Mantenimiento predictivo
Refrigeracion industrial
Metodología scrum
Historias de usuario
Aprendizaje automatico
Aplicación de escritorio
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 2.5 Colombia
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| dc.title.spa.fl_str_mv |
Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning. |
| title |
Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning. |
| spellingShingle |
Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning. Predictive maintenance Industrial refrigeration Scrum methodology User stories Machine learning Desktop application Mantenimiento predictivo Refrigeracion industrial Metodología scrum Historias de usuario Aprendizaje automatico Aplicación de escritorio |
| title_short |
Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning. |
| title_full |
Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning. |
| title_fullStr |
Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning. |
| title_full_unstemmed |
Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning. |
| title_sort |
Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning. |
| dc.creator.fl_str_mv |
Quiroga Niño, Jose Andres |
| dc.contributor.advisor.none.fl_str_mv |
Barrera Gomez, Marien Rocio Alfonso Diaz, Andres Leonardo |
| dc.contributor.author.none.fl_str_mv |
Quiroga Niño, Jose Andres |
| dc.contributor.corporatename.spa.fl_str_mv |
Universidad Santo Tomas |
| dc.subject.keyword.spa.fl_str_mv |
Predictive maintenance Industrial refrigeration Scrum methodology User stories Machine learning Desktop application |
| topic |
Predictive maintenance Industrial refrigeration Scrum methodology User stories Machine learning Desktop application Mantenimiento predictivo Refrigeracion industrial Metodología scrum Historias de usuario Aprendizaje automatico Aplicación de escritorio |
| dc.subject.proposal.spa.fl_str_mv |
Mantenimiento predictivo Refrigeracion industrial Metodología scrum Historias de usuario Aprendizaje automatico Aplicación de escritorio |
| description |
Desarrollo 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. |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-07-13T14:53:40Z |
| dc.date.available.none.fl_str_mv |
2023-07-13T14:53:40Z |
| dc.date.issued.none.fl_str_mv |
2023-06-27 |
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http://purl.org/coar/resource_type/c_bdcc |
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Tesis de maestría |
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info:eu-repo/semantics/acceptedVersion |
| dc.type.drive.none.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| status_str |
acceptedVersion |
| dc.identifier.citation.spa.fl_str_mv |
Alfonso 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. |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/11634/51260 |
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reponame:Repositorio Institucional Universidad Santo Tomás |
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instname:Universidad Santo Tomás |
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repourl:https://repository.usta.edu.co |
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Alfonso 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. reponame:Repositorio Institucional Universidad Santo Tomás instname:Universidad Santo Tomás repourl:https://repository.usta.edu.co |
| url |
http://hdl.handle.net/11634/51260 |
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spa |
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spa |
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Aamir, 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/s12046-017-0688-7 Alfonso Diaz, A. L., Barrera Gomez, M. R., & Quiroga Niño, J. A. (2022). PREDICONFORT 1.0 (No. 1). Universidad Santo Tomas. Allison, P. (2013). What’s the best R-squared for logistic regression. Statistical Horizons, 13. https://statisticalhorizons.com/r2logistic/ Al-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.001 American Society of Heating, R. and A.-C. E. (2021). ASHRAE Handbook: Vol. Fundamentals. ANSI/ASHRAE 55 - 2020. Thermal Environmental Conditions for Human Occupancy, ASHRAE (2020). Bartodziej, C. J. (2017). The Concept Industry 4.0. An Empirical Analysis of Technologies and Applications in Production Logistics. Springer Gabler. Boero, C. (2020). Mantenimiento industrial. Jorge Sarmiento Editor - Universitas. https://elibro.net/es/lc/usta/titulos/172523 Bouabdallaoui, 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/s21041044 Braun, 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. Candanedo, 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. Cerquitelli, T., Nikolakis, N., O’Mahony, N., Macii, E., Ippolito, M., & Makris, S. (Eds.). (2021). Predictive Maintenance in Smart Factories. Springer Singapore. https://doi.org/10.1007/978-981-16-2940-2 Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE). Geoscientific Model Development Discussions, 7(1), 1525–1534. Danfoss A/S. (2017). Datasheet. Pressure Switch and Thermostat. In AI000086439001en-001001 (pp. 1–20). Das, K., Jiang, J., & Rao, J. N. K. (2004). Mean squared error of empirical predictor. The Annals of Statistics, 32(2), 818–840. del 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. Diercks, P., Gläser, D., Lünsdorf, O., Selzer, M., Flemisch, B., & Unger, J. F. (2022). Evaluation of tools for describing, reproducing and reusing scientific workflows. Ebert, C., Abrahamsson, P., & Oza, N. (2012). Lean software development. IEEE Software, 29(05), 22–25. Echavarria 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.mendeley ECOPETROL 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. Elallaoui, 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.2018.04.010 Es-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.2022.07.130 Esteki, 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.v9i3.1905 Fernando, 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). Gelman, A., Goodrich, B., Gabry, J., & Vehtari, A. (2019). R-squared for Bayesian Regression Models. The American Statistician, 73(3), 307–309. https://doi.org/10.1080/00031305.2018.1549100 Gonzalez Ajuech, V. L. (2017). Mantenimiento: tecnicas y aplicaciones industriales. Grupo Editorial Patria. https://elibro.net/es/lc/usta/titulos/40508 Heras 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/122933 Hosamo, 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.2022.111988 Instituto de Hidrología, M. y E. A.-I. (2023). Altas Climatologico. Http://Atlas.Ideam.Gov.Co/VisorAtlasClimatologico.Html. Jimenez Raya, F. (2015). Mantenimiento preventivo de sistemas de automatizacion industrial. ELEM0311. IC Editorial. https://elibro.net/es/lc/usta/titulos/59239 Joshi, A. V. (2020). Machine Learning and Artificial Intelligence. Springer International Publishing. https://doi.org/10.1007/978-3-030-26622-6 Ke, 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. Kubat, M. (2017). An Introduction to Machine Learning. Springer International Publishing. https://doi.org/10.1007/978-3-319-63913-0 L’Esteve, R. C. (2021). The Definitive Guide to Azure Data Engineering. Apress. https://doi.org/10.1007/978-1-4842-7182-7 Martínez, 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). Microsoft. (2022). Documentación de ML.NET. https://learn.microsoft.com/es-es/dotnet/machine-learning/ Microsoft. (2022). Documentación de Visual Studio. https://learn.microsoft.com/es-es/visualstudio/ Microsoft. (2022). Documentacion SQL Server Management Studio (SSMS). https://learn.microsoft.com/es-es/sql/ssms/sql-server-management-studio-ssms Monroy Mejia, M. de los A., & Nava Sanchezllanes, N. (2018). Metodologia de la investigacion. Grupo Editorial Exodo. https://elibro.net/es/lc/usta/titulos/172512 Oficina 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. O’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_3 Ohta, 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.v10i2.551 Panesar, 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_7 Panfilov, P., & Katona, A. (2018). Building Predictive Maintenance Framework for Smart Environment Application Systems (pp. 0460–0470). https://doi.org/10.2507/29th.daaam.proceedings.068 Periyasamy, 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/10.29007/6vwh Rodal Montero, E. (2020). Industria 4.0: conceptos, tecnologias habilitadoras y retos. Difusora Larousse - Ediciones Piramide. https://elibro.net/es/lc/usta/titulos/216140 Salvaris, M., Dean, D., & Tok, W. H. (2018). Deep Learning with Azure. Apress. https://doi.org/10.1007/978-1-4842-3679-6 Sandoval, R. (2022). IoT: La conexión con la industria del mañana. Mundo HVAC&R. Santiago, 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.2019.00019 Santos 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/ Sanzana, 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.2022.104445 Saturno, M., Pertel, V., & Deschamps, F. (2017). Proposal of an automation solutions architecture for Industry 4.0. Sedeño, 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/10.5220/0006220301960203 SIEMENS. (2023). 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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_12 Wautelet, 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_1 WEG. (2023). WEG IOT Platform. https://iot.weg.net/#/portal/home Yang, 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.2017.8066729 Yang, 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.2020.2998586 Yang, 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.2017.10.013 Yang, 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. YORK 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. Yuliansyah, 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/403/1/012085 Zhang, 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.2022.109336 |
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Barrera Gomez, Marien RocioAlfonso Diaz, Andres LeonardoQuiroga Niño, Jose AndresUniversidad Santo Tomas2023-07-13T14:53:40Z2023-07-13T14:53:40Z2023-06-27Alfonso 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.http://hdl.handle.net/11634/51260reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coDesarrollo 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.Development 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.Magister en IngenieríaMaestríaapplication/pdfspaUniversidad Santo TomásMaestría IngenieríaFacultad de Ingeniería ElectrónicaAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Aplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning.Predictive maintenanceIndustrial refrigerationScrum methodologyUser storiesMachine learningDesktop applicationMantenimiento predictivoRefrigeracion industrialMetodología scrumHistorias de usuarioAprendizaje automaticoAplicación de escritorioTesis de maestríainfo:eu-repo/semantics/acceptedVersioninfo:eu-repo/semantics/masterThesishttp://purl.org/coar/resource_type/c_bdccCRAI-USTA TunjaAamir, M., & Khan, M. N. A. (2017). 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