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

共 50 条

[21] An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process
Gao, Xin
Hou, Jian
NEUROCOMPUTING, 2016, 174 : 906 - 911
[22] Tennessee Eastman Process Diagnosis Based on Dynamic Classification With SVDD
Theljani, Foued
Laabidi, Kaouther
Zidi, Salah
Ksouri, Moufida
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME, 2015, 137 (09):
[23] Research on Fault Diagnosis of Tennessee Eastman Process Based on KPCA and SVM
Zhang, Ke
Qian, Kun
Chai, Yi
Li, Yi
Liu, Jianhuan
2014 SEVENTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2014), VOL 2, 2014, : 490 - 495
[24] Fault detection in Tennessee Eastman process with temporal deep learning models
Lomov, Ildar
Lyubimov, Mark
Makarov, Ilya
Zhukov, Leonid E.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 2021, 23
[25] Feature selection for fault detection systems: application to the Tennessee Eastman process
Chebel-Morello, Brigitte
Malinowski, Simon
Senoussi, Hafida
APPLIED INTELLIGENCE, 2016, 44 (01) : 111 - 122
[26] Fault detection of Tennessee Eastman process based on topological features and SVM
Zhao, Huiyang
Hu, Yanzhu
Ai, Xinbo
Hu, Yu
Meng, Zhen
2017 2ND INTERNATIONAL CONFERENCE ON MECHATRONICS AND ELECTRICAL SYSTEMS (ICMES 2017), 2018, 339
[27] Feature selection for fault detection systems: application to the Tennessee Eastman process
Brigitte Chebel-Morello
Simon Malinowski
Hafida Senoussi
Applied Intelligence, 2016, 44 : 111 - 122
[28] Research on Fault Diagnosis of Tennessee Eastman Process Based on KPCA and SVM
Zhang, Ke
Qian, Kun
Chai, Yi
Li, Yi
Liu, Jianhuan
2014 SEVENTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2014), VOL 1, 2014, : 490 - 495
[29] New PCA-based scheme for process fault detection and identification. Application to the Tennessee Eastman process
Guerfel, Mohamed
Ben Aicha, Anissa
Belkhiria, Kamel
Messaoud, Hassani
BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES, 2024, 72 (05)
[30] Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS
Lau, C. K.
Ghosh, Kaushik
Hussain, M. A.
Hassan, C. R. Che
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2013, 120 : 1 - 14

← 1 2 3 4 5 →