A new fault classification approach applied to Tennessee Eastman benchmark process

被引：39

作者：

D'Angelo, Marcos F. S. V. ^{[1
]}

Palhares, Reinaldo M. ^{[2
]}

Camargos Filho, Murilo C. O. ^{[1
]}

Maia, Renato D. ^{[1
]}

Mendes, Joao B. ^{[1
]}

Ekel, Petr Ya. ^{[3
]}

机构：

[1] Univ Estadual Montes Claros, Dept Comp Sci, Av Rui Braga Sn, BR-39401089 Montes Claros, Brazil

[2] Univ Fed Minas Gerais, Dept Elect Engn, Av Antonio Carlos 6627, BR-31270901 Belo Horizonte, MG, Brazil

[3] Pontificia Univ Catolica Minas Gerais, Grad Program Elect Engn, Av Dom Jose Gaspar 500, BR-30535610 Belo Horizonte, MG, Brazil

来源：

APPLIED SOFT COMPUTING | 2016年 / 49卷

关键词：

Fault detection and isolation; Fuzzy/Bayesian approach; Immune/neural formulation; Tennessee Eastman benchmark process; ARTIFICIAL IMMUNE-SYSTEM; QUANTITATIVE MODEL; EXPERT-SYSTEM; DIAGNOSIS; IDENTIFICATION; OBSERVERS; ALGORITHM; DESIGN;

D O I：

10.1016/j.asoc.2016.08.040

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 MetropolisHastings 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. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：676 / 686

页数：11

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