Journal
COMPUTERS & CHEMICAL ENGINEERING
Volume 29, Issue 6, Pages 1217-1223Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2005.02.007
Keywords
PCA; PLS; monitoring; control; digital imaging; machine vision
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This paper gives an overview of methods for utilizing large process data matrices. These data matrices are almost always of less than full statistical rank, and therefore, latent variable methods are shown to be well suited to obtain useful subspace models from them for treating a variety of important industrial problems. An overview of the important concepts behind latent variable models is presented and the methods are illustrated with industrial examples in the following areas: (i) the analysis of historical databases and trouble-shooting process problems; (ii) process monitoring and FDI; (iii) extraction of information from novel multivariate sensors; (iv) process control in reduced dimensional subspaces. In each of these problems, latent variable models provide the framework on which solutions are based. (c) 2005 Elsevier Ltd. All rights reserved.
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