Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors

Producción Científica

Detalles Bibliográficos
Autores: Bazán, Gustavo Henrique, Goedtel, Alessandro, Castoldi, Marcelo Favoretto, Godoy, Wagner Fontes, Duque Pérez, Óscar, Moríñigo Sotelo, Daniel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/58908
Acceso en línea:https://doi.org/10.3390/app11010314
https://uvadoc.uva.es/handle/10324/58908
Access Level:acceso abierto
Palabra clave:Electric motors
Pattern recognition
Bearing failure diagnosis
Artificial bee colony
3306 Ingeniería y Tecnología Eléctricas
3306.03 Motores Eléctricos
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oai_identifier_str oai:uvadoc.uva.es:10324/58908
network_acronym_str ES
network_name_str España
repository_id_str
spelling Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motorsBazán, Gustavo HenriqueGoedtel, AlessandroCastoldi, Marcelo FavorettoGodoy, Wagner FontesDuque Pérez, ÓscarMoríñigo Sotelo, DanielElectric motorsPattern recognitionBearing failure diagnosisArtificial bee colony3306 Ingeniería y Tecnología Eléctricas3306.03 Motores EléctricosProducción CientíficaThree-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.Consejo Nacional de Desarrollo Científico y Tecnológico - (processes 474290/2008-5, 473576/2011-2, 552269/2011-5, 201902/2015-0 and 405228/2016-3)MDPI2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3390/app11010314https://uvadoc.uva.es/handle/10324/58908reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://www.mdpi.com/2076-3417/11/1/314info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:uvadoc.uva.es:10324/589082026-06-13T12:44:47Z
dc.title.none.fl_str_mv Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors
title Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors
spellingShingle Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors
Bazán, Gustavo Henrique
Electric motors
Pattern recognition
Bearing failure diagnosis
Artificial bee colony
3306 Ingeniería y Tecnología Eléctricas
3306.03 Motores Eléctricos
title_short Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors
title_full Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors
title_fullStr Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors
title_full_unstemmed Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors
title_sort Mutual information and meta-heuristic classifiers applied to bearing fault diagnosis in three-phase induction motors
dc.creator.none.fl_str_mv Bazán, Gustavo Henrique
Goedtel, Alessandro
Castoldi, Marcelo Favoretto
Godoy, Wagner Fontes
Duque Pérez, Óscar
Moríñigo Sotelo, Daniel
author Bazán, Gustavo Henrique
author_facet Bazán, Gustavo Henrique
Goedtel, Alessandro
Castoldi, Marcelo Favoretto
Godoy, Wagner Fontes
Duque Pérez, Óscar
Moríñigo Sotelo, Daniel
author_role author
author2 Goedtel, Alessandro
Castoldi, Marcelo Favoretto
Godoy, Wagner Fontes
Duque Pérez, Óscar
Moríñigo Sotelo, Daniel
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Electric motors
Pattern recognition
Bearing failure diagnosis
Artificial bee colony
3306 Ingeniería y Tecnología Eléctricas
3306.03 Motores Eléctricos
topic Electric motors
Pattern recognition
Bearing failure diagnosis
Artificial bee colony
3306 Ingeniería y Tecnología Eléctricas
3306.03 Motores Eléctricos
description Producción Científica
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/app11010314
https://uvadoc.uva.es/handle/10324/58908
url https://doi.org/10.3390/app11010314
https://uvadoc.uva.es/handle/10324/58908
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2076-3417/11/1/314
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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