DocumentCode
3162829
Title
Procedure based on mutual information and bayesian networks for the fault diagnosis of industrial systems
Author
Verron, Sylvain ; Tiplica, Teodor ; Kobi, Abdessamad
Author_Institution
Univ. of Angers, Angers
fYear
2007
fDate
9-13 July 2007
Firstpage
420
Lastpage
425
Abstract
The aim of this paper is to present a new method for process diagnosis using a Bayesian network. The mutual information between each variable of the system and the class variable is computed to identify the important variables. To illustrate the performances of this method, we use the Tennessee Eastman Process. For this complex process (51 variables), we take into account three kinds of faults with the minimal recognition error rate objective.
Keywords
belief networks; fault diagnosis; manufacturing processes; manufacturing systems; pattern classification; Bayesian classifier; Bayesian network; industrial processes; industrial system fault diagnosis; Bayesian methods; Databases; Electrical equipment industry; Fault detection; Fault diagnosis; Mathematical model; Mathematics; Mutual information; Principal component analysis; Process control;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 2007. ACC '07
Conference_Location
New York, NY
ISSN
0743-1619
Print_ISBN
1-4244-0988-8
Electronic_ISBN
0743-1619
Type
conf
DOI
10.1109/ACC.2007.4282400
Filename
4282400
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