Three-phase induction motor fault identification using optimization algorithms and intelligent systems

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorFederal University of Technology-Paraná-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal Institute of Paraná-
Autor(es): dc.creatorGuedes, Jacqueline Jordan-
Autor(es): dc.creatorGoedtel, Alessandro-
Autor(es): dc.creatorCastoldi, Marcelo Favoretto-
Autor(es): dc.creatorSanches, Danilo Sipoli-
Autor(es): dc.creatorSerni, Paulo José Amaral-
Autor(es): dc.creatorRezende, Agnes Fernanda Ferreira-
Autor(es): dc.creatorBazan, Gustavo Henrique-
Autor(es): dc.creatorde Souza, Wesley Angelino-
Data de aceite: dc.date.accessioned2025-08-21T16:10:43Z-
Data de disponibilização: dc.date.available2025-08-21T16:10:43Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s00500-023-09519-5-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308177-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308177-
Descrição: dc.descriptionThe present work proposes the study and development of a strategy that uses an optimization algorithm combined with pattern classifiers to identify short-circuit stator failures, broken rotor bars and bearing wear in three-phase induction motors, using voltage, current, and speed signals. The Differential Evolution, Particle Swarm Optimization, and Simulated Annealing algorithms are used to estimate the electrical parameters of the induction motor through the equivalent electrical circuit and the failure identification arises by variation of these parameters with the evolution of each fault. The classification of each type of failure is tested using Artificial Neural Network, Support Vector Machine and k-Nearest Neighbor. The database used for this work was obtained through laboratory experiments performed with 1-HP and 2-HP line-connected motors, under mechanical load variation and unbalanced voltage.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionElectrical Engineering Department Federal University of Technology-Paraná, Av. Alberto Carazzai, 1640, Paraná-
Descrição: dc.descriptionElectrical Engineering Department São Paulo State University, Av. Eng. Luís Edmundo Carrijo Coube, 14-01, São Paulo-
Descrição: dc.descriptionDepartment of Industrial Process Control Federal Institute of Paraná, Av. Doutor Tito, s/n, Paraná-
Descrição: dc.descriptionElectrical Engineering Department São Paulo State University, Av. Eng. Luís Edmundo Carrijo Coube, 14-01, São Paulo-
Descrição: dc.descriptionCNPq: 307220/2016-8-
Descrição: dc.descriptionCNPq: 473576/2011-2-
Descrição: dc.descriptionCNPq: 474290/2008-3-
Descrição: dc.descriptionCNPq: 552269/2011-5-
Formato: dc.format6709-6724-
Idioma: dc.languageen-
Relação: dc.relationSoft Computing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectFault diagnosis-
Palavras-chave: dc.subjectInduction motors-
Palavras-chave: dc.subjectOptimization methods-
Palavras-chave: dc.subjectPattern classification-
Título: dc.titleThree-phase induction motor fault identification using optimization algorithms and intelligent systems-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.