Quantification analysis for NLPCA-based stiction diagnostic tool
A significant number of control loops in process plants perform poorly due to control valve stiction. Stiction in control valves is the most common and long standing problem in industry, resulting in oscillations in process variables which subsequently lowers product quality and productivity. Develo...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/3738/1/zabirih-nlpcastiction.pdf http://www.scopus.com/record/display.url?origin=recordpage&eid=2-s2.0-64949126466&noHighlight=false&sort=plf-f&src=s&st1=zabiri&st2=h&nlo=1&nlr=20&nls=first-t&sid=E5NmG27IsJfsII9yXMDsTvP%3a73&sot=anl&sdt=aut&sl=37&s=AU-ID%28%22Zabiri%2c+Haslinda%22+196393 http://eprints.utp.edu.my/3738/ |
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| Summary: | A significant number of control loops in process plants perform poorly due to control valve stiction. Stiction in control valves is the most common and long standing problem in industry, resulting in oscillations in process variables which subsequently lowers product quality and productivity. Developing a method to detect valve stiction in the early phase is imperative to avoid major disruptions to the plant operations. In this paper, nonlinear principal component analysis (NLPCA)-based stiction diagnostic tool is presented. Results from simulated case studies show that with proper quantification analysis, NLPCA shows a very promising capability for stiction diagnosis. |
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