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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/1155

Title: Fault detection and isolation using correspondence analysis
Authors: DETROJA, KP
GUDI, RD
PATWARDHAN, SC
ROY, K
Keywords: principal component analysis
pca
diagnosis
Issue Date: 2006
Publisher: AMER CHEMICAL SOC
Citation: INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH,45(1)223-235
Abstract: In this paper, a new approach to fault detection and diagnosis that is based on correspondence analysis (CA) is proposed. CA is a powerful multivariate technique based on the generalized singular value decomposition. The merits of using CA lie in its ability to depict rows as well as columns as points in the dual lower dimensional vector space. CA has been shown to capture association between various features and events quite effectively. The key strengths of CA, for fault detection and diagnosis, are validated on data involving simulations as well as experimental data obtained from a laboratory-scale setup.
URI: http://dx.doi.org/10.1021/ie058033g
http://dspace.library.iitb.ac.in/xmlui/handle/10054/14353
http://hdl.handle.net/100/1155
ISSN: 0888-5885
Appears in Collections:Proceedings papers

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