TY  - CONF
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-77950009735&doi=10.1109%2fICSAP.2009.43&partnerID=40&md5=66e924ab963b3df54bc2182fff05836a
EP  - 188
KW  - Component; Control valve stiction
KW  -  neural network
KW  -  modeling; Control valves; Equipment wear; In-control; Process industries; Product quality; Series-parallel; Stiction models
KW  -  Recurrent neural networks; Robustness (control systems); Safety valves; Signal analysis; Signal processing; System stability
KW  -  Stiction
CY  - Kuala Lumpur
Y1  - 2009///
A1  - Zabiri, H.
A1  - Mazuki, N.
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. 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 IEEE.
SN  - 9780769535944
SP  - 184
ID  - scholars597
TI  - Robustness study on NARXSP-based stiction model
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
ER  -