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Polar classification with correspondence analysis for fault isolation

DSpace at IIT Bombay

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Field Value
 
Title Polar classification with correspondence analysis for fault isolation
 
Creator PUSHA, SONIA
GUDI, RD
NORONHA, SANTOSH
 
Subject sensors
fault detection
principal component analysis
algorithms
 
Description Data collected from operating plants can be mined to extract information related to both normal and fault modes of operation. Correspondence analysis (CA), that decomposes a measure of row–column association, to generate the lower dimensional space has been recently proposed [1] for this task. CA represents the association between samples and variables in terms of angle based measures on a biplot. Thus, toward clearer resolution of the faults, polar clustering and classification procedures are necessary. In this paper, we develop a methodology to mine the operating data and build such clusters. We demonstrate the application of this methodology on data generated from simulations and experiments involving representative systems,for detecting parametric changes and resolving sensor and actuator biases.
 
Publisher Elsevier
 
Date 2009-10-06T04:03:39Z
2011-11-25T15:42:42Z
2011-12-26T13:04:46Z
2011-12-27T05:50:46Z
2009-10-06T04:03:39Z
2011-11-25T15:42:42Z
2011-12-26T13:04:46Z
2011-12-27T05:50:46Z
2009
 
Type Article
 
Identifier Journal of Process Control 19(4), 656-663
0959-1524
http://dx.doi.org/10.1016/j.jprocont.2008.08.003
http://hdl.handle.net/10054/1695
http://dspace.library.iitb.ac.in/xmlui/handle/10054/1695
 
Language en