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Robustness study on NARXSP-based stiction model

Stiction is the most commonly found valve problem in the process industry. Valve stiction may cause oscillations in control loops which increases variability in product quality, accelerates equipment wear and tear, or leads to system instability. In this paper, a series-parallel Recurrent Neural Net...

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Main Authors: H., Zabiri, N., Mazuki
Format: Conference or Workshop Item
Published: 2009
Subjects:
TP Chemical technology
Online Access:http://eprints.utp.edu.my/3736/1/S162.pdf
http://www.scopus.com/record/display.url?origin=recordpage&eid=2-s2.0-77950009735&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/3736/
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spelling my.utp.eprints.37362017-01-19T08:25:41Z Robustness study on NARXSP-based stiction model H., Zabiri N., Mazuki TP Chemical technology Stiction is the most commonly found valve problem in the process industry. Valve stiction may cause oscillations in control loops which increases variability in product quality, accelerates equipment wear and tear, or leads to system instability. In this paper, a series-parallel Recurrent Neural Network (NARXSP)-based stiction model is developed and its robustness against the uncertainty in the stiction parameters is tested under various conditions. It is shown that the NARXSP-based stiction model is robust when the stiction is less than 6% of the valve travel span 2009 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/3736/1/S162.pdf http://www.scopus.com/record/display.url?origin=recordpage&eid=2-s2.0-77950009735&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 H., Zabiri and N., Mazuki (2009) Robustness study on NARXSP-based stiction model. In: 2009 International Conference on Signal Acquisition and Processing, 23 April 2009 through 5 April 2009;, Kuala Lumpur. http://eprints.utp.edu.my/3736/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
topic TP Chemical technology
spellingShingle TP Chemical technology
H., Zabiri
N., Mazuki
Robustness study on NARXSP-based stiction model
description Stiction is the most commonly found valve problem in the process industry. Valve stiction may cause oscillations in control loops which increases variability in product quality, accelerates equipment wear and tear, or leads to system instability. In this paper, a series-parallel Recurrent Neural Network (NARXSP)-based stiction model is developed and its robustness against the uncertainty in the stiction parameters is tested under various conditions. It is shown that the NARXSP-based stiction model is robust when the stiction is less than 6% of the valve travel span
format Conference or Workshop Item
author H., Zabiri
N., Mazuki
author_facet H., Zabiri
N., Mazuki
author_sort H., Zabiri
title Robustness study on NARXSP-based stiction model
title_short Robustness study on NARXSP-based stiction model
title_full Robustness study on NARXSP-based stiction model
title_fullStr Robustness study on NARXSP-based stiction model
title_full_unstemmed Robustness study on NARXSP-based stiction model
title_sort robustness study on narxsp-based stiction model
publishDate 2009
url http://eprints.utp.edu.my/3736/1/S162.pdf
http://www.scopus.com/record/display.url?origin=recordpage&eid=2-s2.0-77950009735&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/3736/
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score 13.18916

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