Diagnosis of Process Nonlinearities and Valve Stiction Data Driven Approaches
Aus der Reihe
Advances in Industrial Control
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- Hardcover
- Taschenbuch ausgewählt
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Sprache:Englisch
Fr. 192.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
25.11.2010
Abbildungen
XX, 313 illus., 115 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Verlag
Springer BerlinSeitenzahl
286
Maße (L/B/H)
23.5/15.5/1.7 cm
Gewicht
470 g
Auflage
Softcover reprint of hardcover 1st ed. 2008
Sprache
Englisch
ISBN
978-3-642-09810-9
were published in the series as the contributed volume, Process Control Performance Assessment: From Theory to Implementation with Andrzej Ordys, Damian Uduehi, and Michael Johnson as Editors (ISBN 978-1-84628-623-0, 2007). Along with this good progress in process controller assessment methods, researchers have also been investigating techniques to diagnose what is causing the process or control loop degradation. This requires the use of on-line data to identify faults via new diagnostic indicators of typical process problems. A significant focus of some of this research has been the issue of valve problems; a research direction that has been motivated by some industrial statistics that show up to 40% of control loops having performance degradation attributable to valve problems. Shoukat Choudhury, Sirish Shah, and Nina Thornhill have been very active in this research field for a number of years and have written a coherent and consistent presentation of their many research results as this monograph, Diagnosis of Process Nonlinearities and Valve Stiction. The Advances in Industrial Control series is pleased to welcome this new and substantial contribution to the process diagnostic literature. The reader will find the exploitation of the extensive process data archives created by today’s process computer systems one theme in the monograph. From another viewpoint, the use of higher-order statistics could be considered to provide a continuing link to the earlier methods of the statistical process control paradigm.
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