NDLI: An inseparability metric to identify a small number of key variables for improved process monitoring

Content Provider	IEEE Xplore Digital Library
Author	Ghosh, K. Srinivasan, R.
Copyright Year	2013
Description	Author affiliation: Nat. Univ. of Singapore, Singapore, Singapore (Ghosh, K.) \|\| Chem. & Biomol. Eng. Dept., Nat. Univ. of Singapore, Singapore, Singapore (Srinivasan, R.)
Abstract	In a large-scale complex chemical process, hundreds of variables are measured. Since statistical process monitoring techniques such as PCA typically involve dimensionality reduction, all measured variables are often provided as input without pre-selection of variables. In our previous work [1], we demonstrated that reduced models based on only a small number of important variables, called key variables, which contain useful information about a fault, can significantly improve performance. This set of key variables is fault specific. In this paper, we propose a metric to identify the key variables of a fault. The metric measures the extent of inseparability in the subspace of a variable subset and thus, provides a reasonable estimate of the monitoring performance for a subset of variables. The excellent ability of the proposed metric in identifying the right key variables is demonstrated through the benchmark Tennessee Eastman Challenge problem.
Starting Page	740
Ending Page	745
File Size	241706
Page Count	6
File Format	PDF
ISBN	9781479915590
ISSN	10851992
DOI	10.1109/CCA.2013.6662838
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-28
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Monitoring Measurement Fault diagnosis Correlation Cooling Process control
Content Type	Text
Resource Type	Article

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