DocumentCode :
1816524
Title :
Artificial neural network approach for detection and diagnosis of valve stiction
Author :
Venceslau, Allan R. S. ; Guedes, Luiz Affonso ; Silva, Diego R. C.
Author_Institution :
Dept. of Comput. Eng. & Autom., Fed. Univ. of Rio Grande do Norte, Rio Grande, Brazil
fYear :
2012
fDate :
17-21 Sept. 2012
Firstpage :
1
Lastpage :
4
Abstract :
Valve stiction or static friction in control loops is a common problem in modern industrial processes. Several recent studies have tried to understand, reproduce, and detect such issue; however, the actual quantification is still a challenge. Since the valve position (mv) is normally unknown in industrial process, the main challenge is to diagnose stiction knowing only the output signals of the process (pv) and the control signal (op). This paper presents an artificial neural network approach in order to detect and quantify the amount of static friction using only the pv and op information. This study was validated by a simulation process. The results show satisfactory measurements of stiction.
Keywords :
mechanical engineering computing; mechanical variables measurement; neural nets; stiction; valves; artificial neural network; control loops; control signal; industrial process; op information; output signals; pv information; static friction; stiction measurements; valve position; valve stiction detection; valve stiction diagnosis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Technologies & Factory Automation (ETFA), 2012 IEEE 17th Conference on
Conference_Location :
Krakow
ISSN :
1946-0740
Print_ISBN :
978-1-4673-4735-8
Electronic_ISBN :
1946-0740
Type :
conf
DOI :
10.1109/ETFA.2012.6489768
Filename :
6489768
Link To Document :
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