TY  - CONF
CY  - Kuala Lumpur
AV  - none
N2  - 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. To help understand and study the behavior of sticky valve, several valve stiction models have been proposed in the literature. In this paper, a black box Neural Network-based modeling approach is proposed to model valve stiction. It is shown that with optimum model structures, performance of the developed NN stiction model is comparable to other established method. © 2009 IEEE.
N1  - cited By 0; Conference of 2009 International Conference on Signal Acquisition and Processing, ICSAP 2009 ; Conference Date: 3 April 2009 Through 5 April 2009; Conference Code:79615
KW  - Black boxes; Component; Control valve stiction
KW  -  neural network
KW  -  modeling; Control valves; Equipment wear; In-control; Neural network model; Optimum model; Process industries; Product quality; Stiction models
KW  -  Earthquake resistance; Model structures; Neural networks; Safety valves; Signal analysis; Signal processing; Structural optimization; System stability
KW  -  Stiction
ID  - scholars598
TI  - Optimization of neural network model structures for valve stiction modeling
SP  - 193
Y1  - 2009///
SN  - 9780769535944
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-77949992585&doi=10.1109%2fICSAP.2009.42&partnerID=40&md5=aaacd76650e7d86fafe4f3b5689218a3
A1  - Zabiri, H.
A1  - Mazuki, N.
EP  - 197
ER  -