A hybrid strategy of using (i) locally linear embedding for nonlinear dimensionality reduction of high dimensional data and (ii) support vector machines for classification of the resultant features is proposed as a robust methodology for process monitoring. Illustrative examples substantiate the methodology vis-à-vis current practice.
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A Hybrid Methodology For On-Line Process Monitoring
1Chemical Engineering Division, National Chemical Laboratory, Pune, India, rhgarg@yahoo.com
2Chemical Engineering Division. National Chemical Laboratory, Pune, India, amjade@rediffmail.com
3Chemical Engineering Division. National Chemical Laboratory, Pune, India, jayaram@che.ncl.res.in
4Chemical Engineering Division. National Chemical Laboratory, Pune, India, bdk@ems.ncl.res.in
Citation Information: International Journal of Chemical Reactor Engineering. Volume 2, Issue 1, Pages –, ISSN (Online) 1542-6580, DOI: 10.2202/1542-6580.1119, May 2004
Publication History:
- Published Online:
- 2004-05-06
Keywords: Process monitoring; locally linear embedding; abnormality detection; support vector domain distribution; novelty detection; single class classification
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