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Evaluación de estrategias de agrupamiento no supervisadas en la determinación de patrones asociados a fallas de sistemas térmicos en tractocamiones graneleros
dc.rights.license | Atribución-NoComercial 4.0 Internacional |
dc.contributor.advisor | Branch Bedoya, John William |
dc.contributor.advisor | Restrepo Martínez, Alejandro |
dc.contributor.author | Zapata Rincón, Andrés Mauricio |
dc.date.accessioned | 2022-06-02T14:02:10Z |
dc.date.available | 2022-06-02T14:02:10Z |
dc.date.issued | 2022-02-28 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/81488 |
dc.description | ilustraciones, diagramas, gráficas, tablas |
dc.description.abstract | Esta tesis tiene como finalidad evaluar estrategias de agrupamiento no supervisadas para datos asociados a tractocamiones graneleros, en la detección de patrones de falla en sistemas térmicos. El estudio de estas técnicas es importante en el ámbito del mantenimiento predictivo basado en datos con la implementación de algoritmos de aprendizaje de máquinas que permitan planificar adecuadamente cronogramas de mantenimiento en empresas de transporte de carga. Para el desarrollo de la tesis, se usa como fuente de información los dispositivos de telemetría de los tractocamiones graneleros de una empresa colombiana de transporte de carga que reportan datos en tiempo real de la medición de variables como velocidad, temperaturas, estado de operación del vehículo, entre otras para el año 2020. También se usa el histórico de ingresos a taller de la flota de 116 tractocamiones donde se analizan los ingresos a taller para la intervención de sistemas térmicos. Estos datos son el insumo para la evaluación de las estrategias de agrupamiento propuestas en este trabajo. Los resultados parten desde la obtención de los datos, preparación de estos y análisis descriptivos para implementar técnicas de reducción de dimensionalidad en la información y posteriormente evaluar el comportamiento de algoritmos de agrupamiento para la detección de patrones de falla que se relacionen a daños en sistemas térmicos de los vehículos. Con el desarrollo de este trabajo se encuentra un potencial para el ahorro en costos correctivos de la flota en taller que apunte a una adecuada gestión de la flota en modelos de pago por uso, apalancando la disponibilidad de los vehículos en las operaciones de transporte. (Texto tomado de la fuente) |
dc.description.abstract | The purpose of this thesis is to evaluate unsupervised clustering strategies for data associated with bulk carrier trucks, in the detection of failure patterns in thermal systems. The study of these techniques is important in the field of data-based predictive maintenance with the implementation of machine learning algorithms that allow proper planning of maintenance schedules in freight transport companies. For the development of the thesis, the telemetry devices of the bulk tractor trucks of a Colombian cargo transport company are used as a source of information, which report data in real time of the measurement of variables such as speed, temperatures, state of operation of the vehicle, among others for the year 2020. The history of workshop entries of the fleet of 116 tractor-trailers is also used, where workshop entries for the intervention of thermal systems are analyzed. These data are the input for the evaluation of the grouping strategies proposed in this work. The results start from obtaining the data, preparing them and descriptive analysis to implement dimensionality reduction techniques in the information and subsequently evaluate the behavior of grouping algorithms for the detection of failure patterns that are related to damage in thermal systems. With the development of this work, there is a potential for savings in corrective costs of the fleet in the workshop that points to an adequate management of the fleet in pay-per-use models, leveraging the availability of vehicles in transport operations. |
dc.format.extent | xvii, 112 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | spa |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación |
dc.subject.ddc | 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
dc.title | Evaluación de estrategias de agrupamiento no supervisadas en la determinación de patrones asociados a fallas de sistemas térmicos en tractocamiones graneleros |
dc.type | Trabajo de grado - Maestría |
dc.type.driver | info:eu-repo/semantics/masterThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Medellín - Minas - Maestría en Ingeniería - Analítica |
dc.contributor.referee | Juan David Ospina Arango |
dc.contributor.researchgroup | Grupo de Promoción E Investigación en Mecánica Aplicada Gpima |
dc.contributor.researchgroup | Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial |
dc.description.degreelevel | Maestría |
dc.description.degreename | Magister en Ingeniería - Analítica |
dc.description.researcharea | Mantenimiento predictivo |
dc.description.researcharea | Análisis de datos |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.department | Departamento de la Computación y la Decisión |
dc.publisher.faculty | Facultad de Minas |
dc.publisher.place | Medellín, Colombia |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Medellín |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.lemb | Camiones - Mantenimiento y reparación |
dc.subject.lemb | Calefacción y ventilación en vehículos |
dc.subject.proposal | Mantenimiento Predictivo |
dc.subject.proposal | Aprendizaje de Máquinas |
dc.subject.proposal | Sistemas térmicos |
dc.subject.proposal | Agrupamiento no supervisado |
dc.subject.proposal | Detección de patrones |
dc.subject.proposal | Predictive Maintenance |
dc.subject.proposal | Machine Learning |
dc.subject.proposal | Thermal Systems |
dc.subject.proposal | Unsupervised Clustering |
dc.subject.proposal | Pattern Detection |
dc.title.translated | Evaluation of unsupervised grouping strategies in the determination of patterns associated with failures of thermal systems in bulk trucks |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/TM |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
dcterms.audience.professionaldevelopment | Estudiantes |
dcterms.audience.professionaldevelopment | Investigadores |
dc.description.curriculararea | Área Curricular de Ingeniería de Sistemas e Informática |
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