Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarilla

ilustraciones, gráficas, tablas

Autores:: Prado Gamba, Lina Fernanda

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

id	UNACIONAL2_bc7edbe6bab81777bbf54420606db5c0
oai_identifier_str	oai:repositorio.unal.edu.co:unal/81638
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarilla
dc.title.translated.eng.fl_str_mv	Machine learning model to structure yellow machinery logs
title	Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarilla
spellingShingle	Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarilla 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Computational linguistics Machine learning Machinery Lingüística computacional Aprendizaje automático (Inteligencia artificial) Maquinaria Mantenimiento Registros de mantenimiento Extraccion de información Aprendizaje de máquina Procesamiento de lenguaje natural Maintenance Maintenance logs Information extraction Machine learning Natural language processing.
title_short	Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarilla
title_full	Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarilla
title_fullStr	Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarilla
title_full_unstemmed	Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarilla
title_sort	Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarilla
dc.creator.fl_str_mv	Prado Gamba, Lina Fernanda
dc.contributor.advisor.spa.fl_str_mv	Gómez Jaramillo, Francisco Albeiro
dc.contributor.author.spa.fl_str_mv	Prado Gamba, Lina Fernanda
dc.contributor.researchgroup.spa.fl_str_mv	Computational Modeling of Biological Systems Research Group - COMBIOS
dc.subject.ddc.spa.fl_str_mv	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
topic	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Computational linguistics Machine learning Machinery Lingüística computacional Aprendizaje automático (Inteligencia artificial) Maquinaria Mantenimiento Registros de mantenimiento Extraccion de información Aprendizaje de máquina Procesamiento de lenguaje natural Maintenance Maintenance logs Information extraction Machine learning Natural language processing.
dc.subject.lemb.eng.fl_str_mv	Computational linguistics Machine learning Machinery
dc.subject.lemb.spa.fl_str_mv	Lingüística computacional Aprendizaje automático (Inteligencia artificial) Maquinaria
dc.subject.proposal.spa.fl_str_mv	Mantenimiento Registros de mantenimiento Extraccion de información Aprendizaje de máquina Procesamiento de lenguaje natural
dc.subject.proposal.eng.fl_str_mv	Maintenance Maintenance logs Information extraction Machine learning Natural language processing.
description	ilustraciones, gráficas, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-06-28T18:37:02Z
dc.date.available.none.fl_str_mv	2022-06-28T18:37:02Z
dc.date.issued.none.fl_str_mv	2022
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/81638
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/81638 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	Akhbardeh, F., Desell, T., and Zampieri, M. (2020a). Maintnet: A collaborative open-source library for predictive maintenance language resources. arXiv preprint arXiv:2005.12443. Akhbardeh, F., Desell, T., and Zampieri, M. (2020b). Nlp tools for predictive maintenance records in maintnet. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations, pages 26–32. Alpaydin, E. (2020). Introduction to machine learning. MIT press. Amazon Web Services, I. (2021). What is data labe- ling? [en l ́ınea]. urlhttps://aws.amazon.com/es/sagemaker/data-labeling/what-is-data- labeling/[5 de Diciembre]. Arif-Uz-Zaman, K., Cholette, M. E., Ma, L., and Karim, A. (2017). Extracting failure time data from industrial maintenance records using text mi- ning. Advanced Engineering Informatics, 33:388–396. Beltrán, J. M. J. (2000). Indicadores de gestion una herramienta para lograr la competitividad. Bishop, C. (2014). Bishop-pattern recognition and machine learning-springer 2006. Antimicrob. Agents Chemother, pages 03728–14. Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146. Bokinsky, H., McKenzie, A., Bayoumi, A., McCaslin, R., Patterson, A., Matthews, M., Schmidley, J., and Eisner, L. (2013). Application of natural language processing techniques to marine v-22 maintenance data for populating a cbm-oriented database. In AHS Airworthiness, CBM, and HUMS Specialists’ Meeting, Huntsville, AL. Bortolini, R. and Forcada, N. (2020). Analysis of building maintenance requests using a text mining approach: Building services evaluation. Building Research & Information, 48(2):207–217. Bouabdallaoui, Y., Lafhaj, Z., Yim, P., Ducoulombier, L., and Bennadji, B. (2020). Natural language processing model for managing maintenance requests in buildings. Buildings, 10(9):160. Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32. Brundage, M. P., Sexton, T., Hodkiewicz, M., Dima, A., and Lukens, S. (2021). Technical language processing: Unlocking maintenance knowledge. Manufacturing Letters, 27:42–46. Butters, J. and Ciravegna, F. (2008). Using similarity metrics for terminology recognition. In LREC. Butters, J. and Ciravegna, F. (2010). Authoring technical documents for effective retrieval. In International Conference on Knowledge Engineering and Knowledge Management, pages 287–300. Springer. Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. d. P., Basto, J. P., and Alcala ́, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137:106024. çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., and Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19):8211. Academias de la Lengua Española y Real Academia Española, A. (2021). Diccionario de la lengua española - edición del tricentenario. [versión 23.5 en línea]. https://dle.rae.es/ [5 de Diciembre]. de Jonge, B. and Scarf, P. A. (2020). A review on maintenance optimization. European journal of operational research, 285(3):805–824. Devaney, M., Ram, A., Qiu, H., and Lee, J. (2005). Preventing failures by mining maintenance logs with case-based reasoning. In Proceedings of the 59th meeting of the society for machinery failure prevention technology (MFPT-59). Ding, S.-H. and Kamaruddin, S. (2015). Maintenance policy optimization—literature review and directions. The international journal of advanced manufacturing technology, 76(5):1263–1283. Ghosh, S., Roy, S., and Bandyopadhyay, S. K. (2012). A tutorial review on text mining algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 1(4):7. Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756. Gunay, H. B., Shen, W., and Yang, C. (2019). Text-mining building maintenance work orders for component fault frequency. Building Research & Information, 47(5):518–533. Hirschberg, J. and Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245):261–266. Kohavi, R. (1998). Glossary of terms. Special issue on applications of machine learning and the knowledge discovery process, 30(271):127–132. Kumar, U., Galar, D., Parida, A., Stenstro ̈m, C., and Berges, L. (2013). Maintenance performance metrics: a state-of-the-art review. Journal of Quality in Main- tenance Engineering. Le, Q. and Mikolov, T. (2014). Distributed representations of sentences and documents. In International conference on machine learning, pages 1188–1196. PMLR. Lundgren, C., Skoogh, A., and Bokrantz, J. (2018). Quantifying the effects of maintenance–a literature review of maintenance models. Procedia CIRP, 72:1305–1310. Luque, C., Luna, J. M., Luque, M., and Ventura, S. (2019). An advanced review on text mining in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3):e1302. López (2019). Your guide to natural language processing (nlp). [en línea]. https://towardsdatascience.com/your-guide-to-natural-languag-processing-nlp- 48ea2511f6e1 [5 de Diciembre]. Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9:381–386. Márquez Vásquez, D. et al. (2011). Plan de negocios de una empresa que brinda servicios de mantenimiento predictivo en colombia. B.S. thesis, Uniandes. Marzec, M., Uhl, T., and Michalak, D. (2014). Verification of text mi- ning techniques accuracy when dealing with urban buses maintenance data. Diagnostyka, 15. Mcallister (2021). Mcallister. [en l ́ınea]. https://mcallister.com.co [5 de Diciembre]. McKenzie, A., Matthews, M., Goodman, N., and Bayoumi, A. (2010). Information extraction from helicopter maintenance records as a springboard for the future of maintenance text analysis. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pages 590–600. Sprin- ger. Nagasaka, M., Sato, M., and Kinoshita, E. (2018). Integrated analy- sis system for elevator optimization maintenance using ontology processing and text mining. In Safety and Reliability–Safe Societies in a Changing World, pages 3093–3098. CRC Press. Nakagawa, T. (2006). Maintenance theory of reliability. Springer Science & Business Media. Ojala, M. and Garriga, G. C. (2010). Permutation tests for studying classifier performance. Journal of Machine Learning Research, 11(6). Olarte, W., Botero, M., and CANON, B. A. (2010). Análisis de vibraciones: una herramienta clave en el mantenimiento predictivo. Scientia et technica, 16(45):219–222. Parida, A. (2006). Maintenance performance measurement system: Application of ict and e-maintenance concepts. International journal of COMADEM, 9(4):30. Pelham, J. G. and Hockley, C. (2017). Analysis of short form maintenance records for nff using nlp, phrase matching, and bayesian learning. Procedia CIRP, 59:257–262. Prasertrungruang, T. and Hadikusumo, B. (2007). Heavy equipment management practices and problems in thai highway contractors. Engineering, construction and Architectural management. Qi, P., Zhang, Y., Zhang, Y., Bolton, J., and Manning, C. D. (2020). Stanza: A python natural language processing toolkit for many human languages. arXiv preprint arXiv:2003.07082. Sánchez Gómez, A. M. et al. (2017). Técnicas de mantenimiento predictivo: metodología de aplicación en las organizaciones. Stenström, C., Al-Jumaili, M., and Parida, A. (2015). Natural language processing of maintenance records data. International Journal of COMADEM, 18(2):33–37. Ubaque Castillo, Y. M., Aguirre Zabala, E. L., et al. (2019). Estructuración del programa de mantenimiento predictivo por condición para los equipos del área de producción en la empresa kellogg de colombia sa. Szücs, B. and Ballagi, (2020). Artificial intelligence in mainte- nance: The industrial application of natural language processing. Usuga Cadavid, J. P., Grabot, B., Lamouri, S., Pellerin, R., and Fortin, A. (2020). Valuing free-form text data from maintenance logs through transfer learning with camembert. Enterprise Information Systems, pages 1–29. Wang, H., Liu, Z., Xu, Y., Wei, X., and Wang, L. (2020). Short text mining framework with specific design for operation and maintenance of power equipment. CSEE Journal of Power and Energy Systems. Yang, C., Chen, Q., Shen, W., and Gunay, B. (2017). Toward failure mode and effect analysis for heating, ventilation and air-conditioning. In 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 408–413. IEEE. Yang, Z., Baraldi, P., and Zio, E. (2020). A novel method for maintenance record clustering and its application to a case study of maintenance optimization. Reliability Engineering & System Safety, 203:107103. Zhang, H. (2004). The optimality of naive bayes. AA, 1(2):3. Zhang, Y., Chen, M., and Liu, L. (2015). A review on text mining. In 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), pages 681–685. IEEE. Zhao, Y., Xu, T.-h., and Hai-feng, W. (2014). Text mining based fault diagnosis of vehicle on-board equipment for high speed railway. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 900–905. IEEE. Unico organismo de normalización en españa (2021). Une-en 13306:2011. [en líınea]. www.une.org[5 de Diciembre].
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	xii, 73 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Maestría en Ciencias - Matemática Aplicada
dc.publisher.department.spa.fl_str_mv	Departamento de Matemáticas
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/81638/5/1032478936.2022.pdf https://repositorio.unal.edu.co/bitstream/unal/81638/6/license.txt https://repositorio.unal.edu.co/bitstream/unal/81638/7/1032478936.2022.pdf.jpg
bitstream.checksum.fl_str_mv	c892a84388b12568b27d3fb0ef6bcc50 8153f7789df02f0a4c9e079953658ab2 82c0f4da1fb9e4aa6716f08851d4fd63
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814089357026394112
spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gómez Jaramillo, Francisco Albeiro415aa92d5615e8a2fa29cfa0a28ec210Prado Gamba, Lina Fernanda04a82d242da29d4e8409b0580f6f48b3Computational Modeling of Biological Systems Research Group - COMBIOS2022-06-28T18:37:02Z2022-06-28T18:37:02Z2022https://repositorio.unal.edu.co/handle/unal/81638Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasLa falta de información relevante para la toma de decisiones es uno de los grandes problemas a los que se enfrentan los departamentos de mantenimiento en las empresas. Esta tesis explora un método automático para aportar a la solución de este problema mediante la extracción de información relevante de los registros históricos de las actividades de mantenimiento realizadas en los equipos. Dada la naturaleza de los datos, texto no estructurado con lenguaje técnico, se plantea la implementación de diferentes representaciones (bag of words, term frequency-inverse document frequency, Fasttext y Doc2vec) para alimentar los modelos de aprendizaje de ma ́quina que realizan la estructuración de información importante contenida en los documentos. En la búsqueda del modelo con mejor rendimiento se compararon modelos de support vector machine, random forest, gaussian naive bayes y gradient boosting trees. Estos modelos se aplicaron a datos provenientes de un negocio de venta y renta de maquinaria amarilla; se consideraron 12 montacargas de 3 modelos diferentes y 4 variables independientes en las cuales se extrae información: tipología, falla encontrada, estado final y sistema. Los modelos con mejor rendimiento alcanzaron un f1-score macro 0,86, 0,8, 0,81 y 0,68 con 3 support vector machine y un gradient boosting trees. Se concluye que para obtener mejores resultados el paso a seguir es aumentar la base de datos y expandir el campo de aplicación. (Texto tomado de la fuente).The lack of relevant information for decision-making is one of the major problems that maintenance departments face. In this thesis, an automatic method is explored to contribute to the solution of this problem by extracting relevant information from the records that are kept of the maintenance activities carried out on the equipment. Given the nature of the data, unstructured text with technical language, the implementation of different representations (bag of words, term frequency–inverse document frequency, Fasttext and Doc2vec) is proposed for the machine learning models that carry out the structuring of relevant information contained in the documents. In the search for the best performing model, support vector machine, random forest, gaussian naive bayes and gradient boosting trees models were compared. The models were applied to data from a business of sale and rental of yellow machinery; 15 forklifts of 3 different models and four independent variables in which information is extracted were considered: typology, fault found, final state and system. The best performing models achieved f1-score macro 0,86, 0,8, 0,81 y 0,68 with 3 support vector.Incluye anexosMaestríaMagíster en Ciencias - Matemática AplicadaAprendizaje de máquinaMatemática aplicadaxii, 73 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - Matemática AplicadaDepartamento de MatemáticasFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasComputational linguisticsMachine learningMachineryLingüística computacionalAprendizaje automático (Inteligencia artificial)MaquinariaMantenimientoRegistros de mantenimientoExtraccion de informaciónAprendizaje de máquinaProcesamiento de lenguaje naturalMaintenanceMaintenance logsInformation extractionMachine learningNatural language processing.Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarillaMachine learning model to structure yellow machinery logsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAkhbardeh, F., Desell, T., and Zampieri, M. (2020a). Maintnet: A collaborative open-source library for predictive maintenance language resources. arXiv preprint arXiv:2005.12443.Akhbardeh, F., Desell, T., and Zampieri, M. (2020b). Nlp tools for predictive maintenance records in maintnet. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations, pages 26–32.Alpaydin, E. (2020). Introduction to machine learning. MIT press.Amazon Web Services, I. (2021). What is data labe- ling? [en l ́ınea]. urlhttps://aws.amazon.com/es/sagemaker/data-labeling/what-is-data- labeling/[5 de Diciembre].Arif-Uz-Zaman, K., Cholette, M. E., Ma, L., and Karim, A. (2017). Extracting failure time data from industrial maintenance records using text mi- ning. Advanced Engineering Informatics, 33:388–396.Beltrán, J. M. J. (2000). Indicadores de gestion una herramienta para lograr la competitividad.Bishop, C. (2014). Bishop-pattern recognition and machine learning-springer 2006. Antimicrob. Agents Chemother, pages 03728–14.Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146.Bokinsky, H., McKenzie, A., Bayoumi, A., McCaslin, R., Patterson, A., Matthews, M., Schmidley, J., and Eisner, L. (2013). Application of natural language processing techniques to marine v-22 maintenance data for populating a cbm-oriented database. In AHS Airworthiness, CBM, and HUMS Specialists’ Meeting, Huntsville, AL.Bortolini, R. and Forcada, N. (2020). Analysis of building maintenance requests using a text mining approach: Building services evaluation. Building Research & Information, 48(2):207–217.Bouabdallaoui, Y., Lafhaj, Z., Yim, P., Ducoulombier, L., and Bennadji, B. (2020). Natural language processing model for managing maintenance requests in buildings. Buildings, 10(9):160.Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.Brundage, M. P., Sexton, T., Hodkiewicz, M., Dima, A., and Lukens, S. (2021). Technical language processing: Unlocking maintenance knowledge. Manufacturing Letters, 27:42–46.Butters, J. and Ciravegna, F. (2008). Using similarity metrics for terminology recognition. In LREC.Butters, J. and Ciravegna, F. (2010). Authoring technical documents for effective retrieval. In International Conference on Knowledge Engineering and Knowledge Management, pages 287–300. Springer.Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. d. P., Basto, J. P., and Alcala ́, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137:106024.çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., and Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19):8211.Academias de la Lengua Española y Real Academia Española, A. (2021). Diccionario de la lengua española - edición del tricentenario. [versión 23.5 en línea]. https://dle.rae.es/ [5 de Diciembre].de Jonge, B. and Scarf, P. A. (2020). A review on maintenance optimization. European journal of operational research, 285(3):805–824.Devaney, M., Ram, A., Qiu, H., and Lee, J. (2005). Preventing failures by mining maintenance logs with case-based reasoning. In Proceedings of the 59th meeting of the society for machinery failure prevention technology (MFPT-59).Ding, S.-H. and Kamaruddin, S. (2015). Maintenance policy optimization—literature review and directions. The international journal of advanced manufacturing technology, 76(5):1263–1283.Ghosh, S., Roy, S., and Bandyopadhyay, S. K. (2012). A tutorial review on text mining algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 1(4):7.Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756.Gunay, H. B., Shen, W., and Yang, C. (2019). Text-mining building maintenance work orders for component fault frequency. Building Research & Information, 47(5):518–533.Hirschberg, J. and Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245):261–266.Kohavi, R. (1998). Glossary of terms. Special issue on applications of machine learning and the knowledge discovery process, 30(271):127–132.Kumar, U., Galar, D., Parida, A., Stenstro ̈m, C., and Berges, L. (2013). Maintenance performance metrics: a state-of-the-art review. Journal of Quality in Main- tenance Engineering.Le, Q. and Mikolov, T. (2014). Distributed representations of sentences and documents. In International conference on machine learning, pages 1188–1196. PMLR.Lundgren, C., Skoogh, A., and Bokrantz, J. (2018). Quantifying the effects of maintenance–a literature review of maintenance models. Procedia CIRP, 72:1305–1310.Luque, C., Luna, J. M., Luque, M., and Ventura, S. (2019). An advanced review on text mining in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3):e1302.López (2019). Your guide to natural language processing (nlp). [en línea]. https://towardsdatascience.com/your-guide-to-natural-languag-processing-nlp- 48ea2511f6e1 [5 de Diciembre].Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9:381–386.Márquez Vásquez, D. et al. (2011). Plan de negocios de una empresa que brinda servicios de mantenimiento predictivo en colombia. B.S. thesis, Uniandes.Marzec, M., Uhl, T., and Michalak, D. (2014). Verification of text mi- ning techniques accuracy when dealing with urban buses maintenance data. Diagnostyka, 15.Mcallister (2021). Mcallister. [en l ́ınea]. https://mcallister.com.co [5 de Diciembre].McKenzie, A., Matthews, M., Goodman, N., and Bayoumi, A. (2010). Information extraction from helicopter maintenance records as a springboard for the future of maintenance text analysis. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pages 590–600. Sprin- ger.Nagasaka, M., Sato, M., and Kinoshita, E. (2018). Integrated analy- sis system for elevator optimization maintenance using ontology processing and text mining. In Safety and Reliability–Safe Societies in a Changing World, pages 3093–3098. CRC Press.Nakagawa, T. (2006). Maintenance theory of reliability. Springer Science & Business Media.Ojala, M. and Garriga, G. C. (2010). Permutation tests for studying classifier performance. Journal of Machine Learning Research, 11(6).Olarte, W., Botero, M., and CANON, B. A. (2010). Análisis de vibraciones: una herramienta clave en el mantenimiento predictivo. Scientia et technica, 16(45):219–222.Parida, A. (2006). Maintenance performance measurement system: Application of ict and e-maintenance concepts. International journal of COMADEM, 9(4):30.Pelham, J. G. and Hockley, C. (2017). Analysis of short form maintenance records for nff using nlp, phrase matching, and bayesian learning. Procedia CIRP, 59:257–262.Prasertrungruang, T. and Hadikusumo, B. (2007). Heavy equipment management practices and problems in thai highway contractors. Engineering, construction and Architectural management.Qi, P., Zhang, Y., Zhang, Y., Bolton, J., and Manning, C. D. (2020). Stanza: A python natural language processing toolkit for many human languages. arXiv preprint arXiv:2003.07082.Sánchez Gómez, A. M. et al. (2017). Técnicas de mantenimiento predictivo: metodología de aplicación en las organizaciones.Stenström, C., Al-Jumaili, M., and Parida, A. (2015). Natural language processing of maintenance records data. International Journal of COMADEM, 18(2):33–37.Ubaque Castillo, Y. M., Aguirre Zabala, E. L., et al. (2019). Estructuración del programa de mantenimiento predictivo por condición para los equipos del área de producción en la empresa kellogg de colombia sa.Szücs, B. and Ballagi, (2020). Artificial intelligence in mainte- nance: The industrial application of natural language processing.Usuga Cadavid, J. P., Grabot, B., Lamouri, S., Pellerin, R., and Fortin, A. (2020). Valuing free-form text data from maintenance logs through transfer learning with camembert. Enterprise Information Systems, pages 1–29.Wang, H., Liu, Z., Xu, Y., Wei, X., and Wang, L. (2020). Short text mining framework with specific design for operation and maintenance of power equipment. CSEE Journal of Power and Energy Systems.Yang, C., Chen, Q., Shen, W., and Gunay, B. (2017). Toward failure mode and effect analysis for heating, ventilation and air-conditioning. In 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 408–413. IEEE.Yang, Z., Baraldi, P., and Zio, E. (2020). A novel method for maintenance record clustering and its application to a case study of maintenance optimization. Reliability Engineering & System Safety, 203:107103.Zhang, H. (2004). The optimality of naive bayes. AA, 1(2):3.Zhang, Y., Chen, M., and Liu, L. (2015). A review on text mining. In 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), pages 681–685. IEEE.Zhao, Y., Xu, T.-h., and Hai-feng, W. (2014). Text mining based fault diagnosis of vehicle on-board equipment for high speed railway. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 900–905. IEEE.Unico organismo de normalización en españa (2021). Une-en 13306:2011. [en líınea]. www.une.org[5 de Diciembre].EstudiantesInvestigadoresORIGINAL1032478936.2022.pdf1032478936.2022.pdfTesis de Maestria en Matemática Aplicadaapplication/pdf12231673https://repositorio.unal.edu.co/bitstream/unal/81638/5/1032478936.2022.pdfc892a84388b12568b27d3fb0ef6bcc50MD55LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81638/6/license.txt8153f7789df02f0a4c9e079953658ab2MD56THUMBNAIL1032478936.2022.pdf.jpg1032478936.2022.pdf.jpgGenerated Thumbnailimage/jpeg4388https://repositorio.unal.edu.co/bitstream/unal/81638/7/1032478936.2022.pdf.jpg82c0f4da1fb9e4aa6716f08851d4fd63MD57unal/81638oai:repositorio.unal.edu.co:unal/816382023-08-04 23:04:21.593Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Modelo de aprendizaje para estructurar los datos de las hojas de vida de maquinaria amarilla

Publicaciones similares