A new scheme for fault detection and classification applied to DC motor

dc.creatorLaércio Ives Santos
dc.creatorReinaldo Martinez Palhares
dc.creatorMarcos Flávio Silveira Vasconcelos D'Angelo
dc.creatorPetr Yakovlevitch Ekel
dc.date.accessioned2025-04-25T13:20:20Z
dc.date.accessioned2025-09-08T23:04:35Z
dc.date.available2025-04-25T13:20:20Z
dc.date.issued2018
dc.identifier.doihttps://doi.org/10.5540/tema.2018.019.02.0327
dc.identifier.issn2179-8451
dc.identifier.urihttps://hdl.handle.net/1843/81843
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofTEMA
dc.rightsAcesso Aberto
dc.subjectControle de processos - Automação
dc.subjectEngenharia Mecânica
dc.subjectControle de Processos
dc.subjectInteligência Computacional
dc.subject.otherFault Detection and Classification 1
dc.subject.otherLuenberger Observer 2
dc.subject.otherParticle Swarm Clustering 3
dc.titleA new scheme for fault detection and classification applied to DC motor
dc.typeArtigo de periódico
local.citation.epage345
local.citation.spage327
local.citation.volume19
local.description.resumoThis study presents an approach for fault detection and classification in a DC drive system. The fault is detected by a classical Luenberger observer. After the fault detection, the fault classification is started. The fault classification, the main contribution of this paper, is based on a representation which combines the Subctrative Clustering algorithm with an adaptation of Particle Swarm Clustering.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.scielo.br/j/tema/a/syRTxnR9KbH3fgKNBscrPMN/?lang=en

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