A Principal Component Approach in Diagnosing poor Control loop performance
Principal component analysis, both linear and nonlinear, are used to identify and remove correlations among process variables as an aid to dimensionality reduction, visualization, and exploratory data analysis. While PCA ascertains only linear correlations between variables, NLPCA reveals both...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/3746/1/Microsoft_Word_-_ICCE_20.pdf http://www.iaeng.org/publication/WCECS2007/WCECS2007_pp194-199. http://eprints.utp.edu.my/3746/ |
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| Summary: | Principal component analysis, both linear and
nonlinear, are used to identify and remove correlations among
process variables as an aid to dimensionality reduction,
visualization, and exploratory data analysis. While PCA
ascertains only linear correlations between variables, NLPCA
reveals both linear and nonlinear correlations, without restriction
on the character of the nonlinearities present in the data. In this
paper, the use of PCA and NLPCA are investigated and
compared for nonlinearity detection in regulated systems using
routine operating data. Results from simulated and industrial
data used in this study clearly show that NLPCA performance
supersedes that of PCA in identifying and detecting nonlinearity
in poor performing control loops. |
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