A new fault classification approach applied to Tennessee Eastman benchmark process

Marcos Flávio Silveira Vasconcelos D"Angelo; Reinaldo Martinez Palhares; Murilo César Osório Camargos; Renato Dourado Maia; João Batista Mendes; Petr Iakovlevitch Ekel

Use este identificador para citar ou linkar para este item: http://hdl.handle.net/1843/42874

Tipo:	Artigo de Periódico
Título:	A new fault classification approach applied to Tennessee Eastman benchmark process
Autor(es):	Marcos Flávio Silveira Vasconcelos D"Angelo Reinaldo Martinez Palhares Murilo César Osório Camargos Renato Dourado Maia João Batista Mendes Petr Iakovlevitch Ekel
Resumo:	This 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.
Assunto:	Engenharia elétrica Algoritmos Redes neurais (Computação) Benchmarking (Administração)
Idioma:	eng
País:	Brasil
Editor:	Universidade Federal de Minas Gerais
Sigla da Instituição:	UFMG
Departamento:	ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
Tipo de Acesso:	Acesso Aberto
Identificador DOI:	https://doi.org/10.1016/j.asoc.2016.08.040
URI:	http://hdl.handle.net/1843/42874
Data do documento:	Dez-2016
metadata.dc.url.externa:	https://www.sciencedirect.com/science/article/pii/S1568494616304343#!
metadata.dc.relation.ispartof:	Applied Soft Computing
Aparece nas coleções:	Artigo de Periódico

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