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Dynamic Kernel Scatter-difference-based discriminant analysis for diagnosis of Tennessee Eastman process

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Title Dynamic Kernel Scatter-difference-based discriminant analysis for diagnosis of Tennessee Eastman process
 
Creator SUMANA, C
VENKATESWARLU, C
GUDI, RD
BHUSHAN, M
 
Subject principal component analysis
support vector machines
partial least-squares
fault-diagnosis
identification
pca
 
Description A dynamic kernel scatter-difference-based discriminant analysis (DKSDA) method, that addresses overlapping and auto-correlated data resulting from different types of abnormal situations, is proposed here for fault diagnosis of nonlinear chemical processes. The proposed method is based on scatter-difference-based discriminant analysis performed in a high dimensional nonlinear feature space that is obtained via nonlinear kernel transformation of a suitably lagged, dynamic representation of the variables. The DKSDA overcomes the singularity problem of within-class-scatter matrix that is encountered in kernel Fisher discriminant analysis (KFDA), by considering scatter difference form of the Fisher criterion. Fault diagnosis is performed by scores classification using the nearest neighbor classifier in DKSDA space. The performance of the proposed method is evaluated by applying it for the isolation of complex faults in the Tennessee Eastman process. The results demonstrate the superiority of the DKSDA over other recently reported nonlinear classification methods.
 
Publisher IEEE
 
Date 2011-10-25T10:15:14Z
2011-12-15T09:11:47Z
2011-10-25T10:15:14Z
2011-12-15T09:11:47Z
2009
 
Type Proceedings Paper
 
Identifier 2009 AMERICAN CONTROL CONFERENCE, VOLS 1-9,3417-3422
978-1-4244-4523-3
0743-1619
http://dspace.library.iitb.ac.in/xmlui/handle/10054/15686
http://hdl.handle.net/100/2286
 
Source American Control Conference 2009,St Louis, MO,JUN 10-12, 2009
 
Language English