dc.contributorBarrera Gomez, Marien Rocio
dc.contributorAlfonso Diaz, Andres Leonardo
dc.contributorUniversidad Santo Tomas
dc.creatorQuiroga Niño, Jose Andres
dc.date.accessioned2023-07-13T14:53:40Z
dc.date.accessioned2023-09-06T13:16:28Z
dc.date.available2023-07-13T14:53:40Z
dc.date.available2023-09-06T13:16:28Z
dc.date.created2023-07-13T14:53:40Z
dc.date.issued2023-06-27
dc.identifierAlfonso 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.identifierhttp://hdl.handle.net/11634/51260
dc.identifierreponame:Repositorio Institucional Universidad Santo Tomás
dc.identifierinstname:Universidad Santo Tomás
dc.identifierrepourl:https://repository.usta.edu.co
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8679874
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.
dc.languagespa
dc.publisherUniversidad Santo Tomás
dc.publisherMaestría Ingeniería
dc.publisherFacultad de Ingeniería Electrónica
dc.relationAamir, 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
dc.relationAlfonso Diaz, A. L., Barrera Gomez, M. R., & Quiroga Niño, J. A. (2022). PREDICONFORT 1.0 (No. 1). Universidad Santo Tomas.
dc.relationAllison, P. (2013). What’s the best R-squared for logistic regression. Statistical Horizons, 13. https://statisticalhorizons.com/r2logistic/
dc.relationAl-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
dc.relationAmerican Society of Heating, R. and A.-C. E. (2021). ASHRAE Handbook: Vol. Fundamentals.
dc.relationANSI/ASHRAE 55 - 2020. Thermal Environmental Conditions for Human Occupancy, ASHRAE (2020).
dc.relationBartodziej, C. J. (2017). The Concept Industry 4.0. An Empirical Analysis of Technologies and Applications in Production Logistics. Springer Gabler.
dc.relationBoero, C. (2020). Mantenimiento industrial. Jorge Sarmiento Editor - Universitas. https://elibro.net/es/lc/usta/titulos/172523
dc.relationBouabdallaoui, 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
dc.relationBraun, 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.
dc.relationCandanedo, 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.
dc.relationCerquitelli, 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
dc.relationChai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE). Geoscientific Model Development Discussions, 7(1), 1525–1534.
dc.relationDanfoss A/S. (2017). Datasheet. Pressure Switch and Thermostat. In AI000086439001en-001001 (pp. 1–20).
dc.relationDas, K., Jiang, J., & Rao, J. N. K. (2004). Mean squared error of empirical predictor. The Annals of Statistics, 32(2), 818–840.
dc.relationdel 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.
dc.relationDiercks, P., Gläser, D., Lünsdorf, O., Selzer, M., Flemisch, B., & Unger, J. F. (2022). Evaluation of tools for describing, reproducing and reusing scientific workflows.
dc.relationEbert, C., Abrahamsson, P., & Oza, N. (2012). Lean software development. IEEE Software, 29(05), 22–25.
dc.relationEchavarria 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
dc.relationECOPETROL 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.
dc.relationElallaoui, 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
dc.relationEs-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
dc.relationEsteki, 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
dc.relationFernando, 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).
dc.relationGelman, 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
dc.relationGonzalez Ajuech, V. L. (2017). Mantenimiento: tecnicas y aplicaciones industriales. Grupo Editorial Patria. https://elibro.net/es/lc/usta/titulos/40508
dc.relationHeras 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
dc.relationHosamo, 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
dc.relationInstituto de Hidrología, M. y E. A.-I. (2023). Altas Climatologico. Http://Atlas.Ideam.Gov.Co/VisorAtlasClimatologico.Html.
dc.relationJimenez Raya, F. (2015). Mantenimiento preventivo de sistemas de automatizacion industrial. ELEM0311. IC Editorial. https://elibro.net/es/lc/usta/titulos/59239
dc.relationJoshi, A. V. (2020). Machine Learning and Artificial Intelligence. Springer International Publishing. https://doi.org/10.1007/978-3-030-26622-6
dc.relationKe, 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.
dc.relationKubat, M. (2017). An Introduction to Machine Learning. Springer International Publishing. https://doi.org/10.1007/978-3-319-63913-0
dc.relationL’Esteve, R. C. (2021). The Definitive Guide to Azure Data Engineering. Apress. https://doi.org/10.1007/978-1-4842-7182-7
dc.relationMartínez, 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).
dc.relationMicrosoft. (2022). Documentación de ML.NET. https://learn.microsoft.com/es-es/dotnet/machine-learning/
dc.relationMicrosoft. (2022). Documentación de Visual Studio. https://learn.microsoft.com/es-es/visualstudio/
dc.relationMicrosoft. (2022). Documentacion SQL Server Management Studio (SSMS). https://learn.microsoft.com/es-es/sql/ssms/sql-server-management-studio-ssms
dc.relationMonroy Mejia, M. de los A., & Nava Sanchezllanes, N. (2018). Metodologia de la investigacion. Grupo Editorial Exodo. https://elibro.net/es/lc/usta/titulos/172512
dc.relationOficina 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.
dc.relationO’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
dc.relationOhta, 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
dc.relationPanesar, 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
dc.relationPanfilov, 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
dc.relationPeriyasamy, 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
dc.relationRodal Montero, E. (2020). Industria 4.0: conceptos, tecnologias habilitadoras y retos. Difusora Larousse - Ediciones Piramide. https://elibro.net/es/lc/usta/titulos/216140
dc.relationSalvaris, M., Dean, D., & Tok, W. H. (2018). Deep Learning with Azure. Apress. https://doi.org/10.1007/978-1-4842-3679-6
dc.relationSandoval, R. (2022). IoT: La conexión con la industria del mañana. Mundo HVAC&R.
dc.relationSantiago, 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
dc.relationSantos 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/
dc.relationSanzana, 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
dc.relationSaturno, M., Pertel, V., & Deschamps, F. (2017). Proposal of an automation solutions architecture for Industry 4.0.
dc.relationSedeño, 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
dc.relationSIEMENS. (2023). SIEMENS MindSphere. https://new.siemens.com/co/es/productos/software/mindsphere.html
dc.relationSrinivasan, 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/10.26868/25222708.2017.715
dc.relationStanfield, C., & Skaves, D. (2013). Fundamentals of HVACR (A. Vernon R, Ed.; 2nd ed.). PEARSON.
dc.relationSubih, 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).
dc.relationSukhodolov, 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_1
dc.relationTaibi, 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_5
dc.relationTrivedi, 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/ISAP48318.2019.9065995
dc.relationUrbieta, 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.2020.106375
dc.relationVijayaraghavan, 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_12
dc.relationWautelet, 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
dc.relationWEG. (2023). WEG IOT Platform. https://iot.weg.net/#/portal/home
dc.relationYang, 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
dc.relationYang, 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
dc.relationYang, 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
dc.relationYang, 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.
dc.relationYORK 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.
dc.relationYuliansyah, 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
dc.relationZhang, 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
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.rightsAbierto (Texto Completo)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia
dc.titleAplicación de escritorio para mantenimiento predictivo de equipos industriales de refrigeración a través de machine learning.


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