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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/42874
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dc.creatorMarcos Flávio Silveira Vasconcelos D"Angelopt_BR
dc.creatorReinaldo Martinez Palharespt_BR
dc.creatorMurilo César Osório Camargospt_BR
dc.creatorRenato Dourado Maiapt_BR
dc.creatorJoão Batista Mendespt_BR
dc.creatorPetr Iakovlevitch Ekelpt_BR
dc.date.accessioned2022-07-04T14:20:10Z-
dc.date.available2022-07-04T14:20:10Z-
dc.date.issued2016-12-
dc.citation.volume49pt_BR
dc.citation.spage676pt_BR
dc.citation.epage686pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2016.08.040pt_BR
dc.identifier.issn15684946pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/42874-
dc.description.resumoThis study presents a data-based methodology for fault detection and isolation in dynamic systems based on fuzzy/Bayesian approach for change point detection associated with a hybrid immune/neural formulation for pattern classification applied to the Tennessee Eastman benchmark process. The fault is detected when a change occurs in the signals from the sensors and classified into one of the classes by the immune/neural formulation. The change point detection system is based on fuzzy set theory associated with the Metropolis–Hastings algorithm and the classification system, the main contribution of this paper is based on a representation which combines the ClonALG algorithm with the Kohonen neural network.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Geraispt_BR
dc.description.sponsorshipOutra Agênciapt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofApplied Soft Computingpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectClassificação de falhaspt_BR
dc.subjectDetecção de Falhaspt_BR
dc.subjectDiagnóstico de Falhaspt_BR
dc.subjectAbordagem Imunoinspiradapt_BR
dc.subjectAbordagem Fuzzy/Bayesianapt_BR
dc.subjectInteligência Computacionalpt_BR
dc.subject.otherEngenharia elétricapt_BR
dc.subject.otherAlgoritmospt_BR
dc.subject.otherRedes neurais (Computação)pt_BR
dc.subject.otherBenchmarking (Administração)pt_BR
dc.titleA new fault classification approach applied to Tennessee Eastman benchmark processpt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.sciencedirect.com/science/article/pii/S1568494616304343#!pt_BR
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