A new fault classification approach applied to Tennessee Eastman benchmark process

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dc.creatorMarcos Flávio Silveira Vasconcelos D"Angelo
dc.creatorReinaldo Martinez Palhares
dc.creatorMurilo César Osório Camargos
dc.creatorRenato Dourado Maia
dc.creatorJoão Batista Mendes
dc.creatorPetr Iakovlevitch Ekel
dc.date.accessioned2022-07-04T14:20:10Z
dc.date.accessioned2025-09-08T23:46:58Z
dc.date.available2022-07-04T14:20:10Z
dc.date.issued2016-12
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
dc.description.sponsorshipOutra Agência
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2016.08.040
dc.identifier.issn15684946
dc.identifier.urihttps://hdl.handle.net/1843/42874
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofApplied Soft Computing
dc.rightsAcesso Aberto
dc.subjectEngenharia elétrica
dc.subjectAlgoritmos
dc.subjectRedes neurais (Computação)
dc.subjectBenchmarking (Administração)
dc.subject.otherClassificação de falhas
dc.subject.otherDetecção de Falhas
dc.subject.otherDiagnóstico de Falhas
dc.subject.otherAbordagem Imunoinspirada
dc.subject.otherAbordagem Fuzzy/Bayesiana
dc.subject.otherInteligência Computacional
dc.titleA new fault classification approach applied to Tennessee Eastman benchmark process
dc.typeArtigo de periódico
local.citation.epage686
local.citation.spage676
local.citation.volume49
local.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.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S1568494616304343#!

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