Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos

ilustraciones, diagramas

Autores:
Velandia Cardenas, Diego Alexander
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/82862
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/82862
https://repositorio.unal.edu.co/
Palabra clave:
600 - Tecnología (Ciencias aplicadas)
000 - Ciencias de la computación, información y obras generales
Procesamiento de datos
Data processing
Aprendizaje supervisado
Data-driven
Detección
Fallas
FDI
Machine Learning
Sensor
Termopar
XGBoost
Supervised learning
Data-driven
Detection
Failures
Machine Learning
Sensor
Thermocouple
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_b354a3efdd011ab1c586aacff768d7a2
oai_identifier_str oai:repositorio.unal.edu.co:unal/82862
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
dc.title.translated.eng.fl_str_mv Fault detection and isolation in a thermocouple network by data-driven techniques
title Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
spellingShingle Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
600 - Tecnología (Ciencias aplicadas)
000 - Ciencias de la computación, información y obras generales
Procesamiento de datos
Data processing
Aprendizaje supervisado
Data-driven
Detección
Fallas
FDI
Machine Learning
Sensor
Termopar
XGBoost
Supervised learning
Data-driven
Detection
Failures
Machine Learning
Sensor
Thermocouple
title_short Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
title_full Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
title_fullStr Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
title_full_unstemmed Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
title_sort Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
dc.creator.fl_str_mv Velandia Cardenas, Diego Alexander
dc.contributor.advisor.none.fl_str_mv Sofrony Esmeral, Jorge
dc.contributor.author.none.fl_str_mv Velandia Cardenas, Diego Alexander
dc.contributor.subjectmatterexpert.none.fl_str_mv López Pulgarín, Erwin José
dc.contributor.orcid.spa.fl_str_mv 0000-0003-4835-1996
dc.contributor.cvlac.spa.fl_str_mv Velandia Cárdenas, Diego Alexander
dc.contributor.researchgate.spa.fl_str_mv Diego_Velandia
dc.contributor.googlescholar.spa.fl_str_mv 364MrlgAAAAJ
dc.subject.ddc.spa.fl_str_mv 600 - Tecnología (Ciencias aplicadas)
000 - Ciencias de la computación, información y obras generales
topic 600 - Tecnología (Ciencias aplicadas)
000 - Ciencias de la computación, información y obras generales
Procesamiento de datos
Data processing
Aprendizaje supervisado
Data-driven
Detección
Fallas
FDI
Machine Learning
Sensor
Termopar
XGBoost
Supervised learning
Data-driven
Detection
Failures
Machine Learning
Sensor
Thermocouple
dc.subject.lemb.spa.fl_str_mv Procesamiento de datos
dc.subject.lemb.eng.fl_str_mv Data processing
dc.subject.proposal.spa.fl_str_mv Aprendizaje supervisado
Data-driven
Detección
Fallas
FDI
Machine Learning
Sensor
Termopar
XGBoost
dc.subject.proposal.eng.fl_str_mv Supervised learning
Data-driven
Detection
Failures
Machine Learning
Sensor
Thermocouple
description ilustraciones, diagramas
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-12-13T21:41:54Z
dc.date.available.none.fl_str_mv 2022-12-13T21:41:54Z
dc.date.issued.none.fl_str_mv 2022-12-07
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_71e4c1898caa6e32
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/82862
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/82862
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
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dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
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spelling Atribución-NoComercial 4.0 Internacionalinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sofrony Esmeral, Jorgee64c2a109fa22579af8b1761776daf31600Velandia Cardenas, Diego Alexander871fe9e2c7e04d6941f49811800dc8f8600López Pulgarín, Erwin José0000-0003-4835-1996Velandia Cárdenas, Diego AlexanderDiego_Velandia364MrlgAAAAJ2022-12-13T21:41:54Z2022-12-13T21:41:54Z2022-12-07https://repositorio.unal.edu.co/handle/unal/82862Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEn el presente documento se explica el proceso de desarrollo de un modelo para detección y aislamiento de fallos (FDI ) en una red de termopares mediante técnicas basadas en datos. El documento inicia describiendo generalidades del funcionamiento de la planta, conceptos básicos sobre termopares, definición de FDI, su relevancia en la planta y el modo en que este se desarrolla actualmente, lo cual abre paso a la identificación del problema y el planteamiento de los objetivos. El desarrollo del proyecto se divide en 4 etapas, iniciando con el reconocimiento del conjunto de datos disponibles, seguido de un estudio de métricas obtenidas a partir del conjunto de datos y su relación con estados de fallo o normalidad en los termopares, establecimiento de una metodología para el entrenamiento de modelos basados en datos y los resultados obtenidos de su aplicación. El documento finaliza con la determinación de parámetros para la construcción un modelo basado en datos que muestra una precisión superior al 76 %, según pruebas de validación aplicadas, entre otras conclusiones obtenidas del desarrollo del presente proyecto. (Texto tomado de la fuente)The present document explains the development process of a fault detection and isolation (FDI) model for a thermocouple network by data-driven techniques. The begins by describing plant’s functioning generals, thermocouples’ basic concepts, FDI definition, its relevance for the plant and how it is currently performed, which allows the problem’s identification and objectives definition. The project’s development divides into 4 stages, starting by a reconnaissance of available data, followed by a study of metrics obtained from the data set and their linkage to thermocouple’s in fail or normality statuses, establishment of a methodology for training data-driven models and its results. The document finalizes by determining the parameters for the constructions of a data-driven model showing an accuracy over 76 %, according to applied validation tests, among other conclusions from the development of the present project.El presente trabajo fue realizado dentro del marco de la colaboración entre la Universidad Nacional de Colombia y Cerro Matoso S.A, financiada por el Ministerio Colombiano de Ciencia mediante la convocatoria 786: “Convocatoria para el registro de proyectos que aspiran a obtener beneficios tributarios por inversi´on en CTel“. La totalidad de los registros empleados en el presente proyecto son de carácter privado y pertenecen a Cerro Matoso S.A. Dichos registros no pueden ser publicados, compartidos o reproducidos total o parcialmente sin el conocimiento y expresa autorización de Cerro Matoso S.A.MaestríaMagíster en Ingeniería - Automatización Industrialix, 130 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Automatización IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá600 - Tecnología (Ciencias aplicadas)000 - Ciencias de la computación, información y obras generalesProcesamiento de datosData processingAprendizaje supervisadoData-drivenDetecciónFallasFDIMachine LearningSensorTermoparXGBoostSupervised learningData-drivenDetectionFailuresMachine LearningSensorThermocoupleDetección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en DatosFault detection and isolation in a thermocouple network by data-driven techniquesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesishttp://purl.org/coar/version/c_71e4c1898caa6e32Texthttp://purl.org/redcol/resource_type/TMCMSA, PR032018OP - Manual del Sistema de Control Estructural del Horno Eléctrico 412-FC-01, 02 ed., 2017.L. 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Balas, “Wind turbine fault detection using counterbased residual thresholding,” IFAC Proceedings Volumes (IFAC-PapersOnline), vol. 44, pp. 8289–8294, 2011.EstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82862/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1019054912_2022.pdf1019054912_2022.pdfTesis de Maestría en Ingeniería - Automatización Industrialapplication/pdf14369433https://repositorio.unal.edu.co/bitstream/unal/82862/4/1019054912_2022.pdf3f87fd655443881af9e141a926b98da9MD54THUMBNAIL1019054912_2022.pdf.jpg1019054912_2022.pdf.jpgGenerated Thumbnailimage/jpeg4481https://repositorio.unal.edu.co/bitstream/unal/82862/5/1019054912_2022.pdf.jpg990929e46ac268c27a7807e6a6a5ccadMD55unal/82862oai:repositorio.unal.edu.co:unal/828622023-08-11 23:04:39.835Repositorio Institucional Universidad Nacional de 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