Diagnosis of process nonlinearities and valve stiction : data driven approaches
In this book, Higher Order Statistical (HOS) theory is used to develop indices for detecting and quantifying signal non-Gaussianity and nonlinearity. These indices, together with specific patterns in the mapping of process output and controller output are used to diagnose the causes of poor control...
Gardado en:
| Auteurs principaux: | , , , |
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| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2008.
Cham : Springer Nature |
| Series: | Advances in Industrial Control
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| Acceso en liña: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Diagnosis of Process Nonlinearities and Valve Stiction, Texte imprimé, 9783540792239 • Diagnosis of Process Nonlinearities and Valve Stiction, Texte imprimé, 9783540872023 • Diagnosis of Process Nonlinearities and Valve Stiction, Texte imprimé, 9783642098109 |
| Résumé: | In this book, Higher Order Statistical (HOS) theory is used to develop indices for detecting and quantifying signal non-Gaussianity and nonlinearity. These indices, together with specific patterns in the mapping of process output and controller output are used to diagnose the causes of poor control loop performance. Often valve stiction is the main cause of poor control performance. A generalized definition of valve stiction based on the investigation of real plant data is proposed. A simple data-driven model of valve stiction is developed. The model is simple, yet powerful enough to properly simulate the complex valve stiction phenomena. Both open and closed loop results have been presented and validated to show the capability of the model. Conventional invasive methods such as the valve travel test can detect stiction easily. However, they are expensive, time consuming and tedious to use for examining thousands of valves in a typical process industry. A non-invasive method that can simultaneously detect and quantify control valve stiction is presented. The method requires only routine operating data from the process. Over a dozen industrial case studies have demonstrated the wide applicability and practicality of this method. In chemical industrial practice, data are often compressed for archival purposes, using various techniques. Compression degrades data quality and induces nonlinearity in the data. The issues of data quality degradation and nonlinearity induction due to compression are investigated in this book. An automatic method for detection and quantification of the compression present in the archived data is discussed. Compelling and quantitative analyses have been recommended to end the practice of process data compression. |
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| descrición da copia: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783540792246 |
| ISSN: | 2193-1577 |
| Acceso: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |