Characterization of faults in rotating machines using multivariate component analysis

dc.contributorCastellanos-Domínguez, César Germán
dc.contributorCardona-Morales, Óscar
dc.contributorGrupo de Control y Procesamiento Digital de Señales
dc.creatorRuales-Torres, Anderson Alberto
dc.date.accessioned2020-05-13T22:09:23Z
dc.date.accessioned2022-09-21T18:14:51Z
dc.date.available2020-05-13T22:09:23Z
dc.date.available2022-09-21T18:14:51Z
dc.date.created2020-05-13T22:09:23Z
dc.date.issued2020
dc.identifierRuales-Torres, A. A. 2020. Characterization of faults in rotating machines using multivariate component analysis. masters, Universidad Nacional de Colombia-Sede Manizales.
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/77515
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3406955
dc.description.abstractThis thesis aims to develop a set of methodologies that allow the feature extraction and blind source separation, to diagnose the different types of faults in gearboxes and bearings. First, it is proposed the implementation of fault detection algorithms in a gearbox with 10 fault types, where it is obtained success percentages above 85 %. Thus, the results of the classification show that feature extraction methodology is significant. Second, an analysis of different methods of blind source separation is realized, which highlights the characteristic frequencies of the fault, showing that they are a useful tool in the identification. Finally, multiple-constrained Independent Component Analysis mcICA is proposed, taking advantage of the information encoded by the signal envelope to fault localization. Therefore, the methodology discussed in this document provides both the evaluation of the state of health and the machine maintenance process. (Texto tomado de la fuente)
dc.description.abstractLa presente tesis pretende desarrollar un conjunto de metodología que permitan la extracción de características y la separación de fuentes ocultas, con el fin de diagnosticar los distintos tipos de fallos en cajas de engranajes y rodamientos. Primero, se propone la implementación de algoritmos para la detección de fallas en una caja de engranajes con 10 tipos de fallas, donde se obtuvo porcentajes de acierto por encima del 85 %, así, los resultados de clasificación muestran que la metodología empleada para la extracción de características es significativa. Segundo, se realiza un análisis de diferentes métodos de separación de fuentes ocultas, los cuales logran resaltar las frecuencias características de la falla, evidenciando que son una herramienta útil en la identificación. Finalmente, se propone un algoritmo de Análisis de Componentes Independientes basado en múltiples restricciones mcICA, aprovechando la información codificada por la envolvente de la señal para localizar la falla. Por lo tanto, la metodología tratada en este documento contribuye tanto a la evaluación del estado de salud como al proceso de mantenimiento de la máquina.
dc.languageeng
dc.publisherManizales - Ingeniería y Arquitectura - Maestría en Ingeniería - Automatización Industrial
dc.publisherDepartamento de Ingeniería Eléctrica y Electrónica
dc.publisherUniversidad Nacional de Colombia - Sede Manizales
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dc.rightsAtribución-SinDerivadas 4.0 Internacional
dc.rightsAcceso abierto
dc.rightshttp://creativecommons.org/licenses/by-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.titleCharacterization of faults in rotating machines using multivariate component analysis
dc.typeTesis


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