Real-time fault diagnosis of nonlinear systems

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorLeite, Daniel F.-
Autor(es): dc.creatorHell, Michel B.-
Autor(es): dc.creatorCosta Junior, Pyramo-
Autor(es): dc.creatorGomide, Fernando-
Data de aceite: dc.date.accessioned2026-02-09T11:41:14Z-
Data de disponibilização: dc.date.available2026-02-09T11:41:14Z-
Data de envio: dc.date.issued2017-08-31-
Data de envio: dc.date.issued2017-08-31-
Data de envio: dc.date.issued2009-12-15-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/15298-
Fonte completa do material: dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0362546X09007809#!-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1145204-
Descrição: dc.descriptionThis paper concerns the development of a real-time fault detection and diagnosis system for a class of electrical machines. Changes in the system dynamics due to a fault are detected using nonlinear models, namely, nonlinear functions of the measurable variables. At the core of the fault detection and diagnosis system are artificial neural networks and a new neural network structure designed to capture temporal information in the input data. Difficulties such as voltage unbalance, measurement noise, and variable loads, commonly found in practice, are overcome by the system addressed in this paper. Because false alarms are significantly reduced and the system is robust to parameter variations, high detection and diagnosis performance are achieved during both, learning and testing phases. Experimental results using actual data are included to show the effectiveness of the real-time fault detection system developed.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceNonlinear Analysis: Theory, Methods & Applications-
Palavras-chave: dc.subjectFault diagnosis-
Palavras-chave: dc.subjectArtificial neural network-
Palavras-chave: dc.subjectElectrical machines-
Palavras-chave: dc.subjectReal-time-
Palavras-chave: dc.subjectDiagnóstico de falhas-
Palavras-chave: dc.subjectRede neural artificial-
Palavras-chave: dc.subjectMáquinas elétrica-
Palavras-chave: dc.subjectTempo real-
Título: dc.titleReal-time fault diagnosis of nonlinear systems-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

Não existem arquivos associados a este item.