eprintid: 10191
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/01/91
datestamp: 2023-11-09 16:36:49
lastmod: 2023-11-09 16:36:49
status_changed: 2023-11-09 16:30:48
type: article
metadata_visibility: show
creators_name: Samat, N.A.S.A.
creators_name: Zabiri, H.
creators_name: Kamaruddin, B.
title: The development and investigation analysis of an arx-based generalized likelihood ratio (GLR) stiction detection method
ispublished: pub
note: cited By 0
abstract: Control valve stiction is one of the main sources of nonlinearity which can result in many deleterious effects on the control loop performance of a process. The study of stiction detection methods has now becoming one of the essential research areas in process control. In this present work, an ARX-based Generalized Likelihood Ratio (GLR) stiction detection method is proposed and its effectiveness is analyzed. The implementation of the proposed method involves three main stages; 1) ARX model identification, 2) GLR test, and 3) statistical hypothesis testing. The proposed detection method was applied to two benchmark simulated case studies. Results showed that the method effectively detect stiction. The presence of stiction is declared if the GLR test statistics, L(R) exceeds the decision threshold limit, h(α)=3.841, and the null hypothesis is rejected at 5 significance level. On the other hand, if L(R) value lies below h(α)=3.841, the null hypothesis is accepted and the absence of stiction is confirmed. In addition, it is also observed that the proposed method is reasonably insensitive and robust to the changes in the process gain, K and time constant, � as it generally allows up to ±10 changes in the two parameters for both case studies. © 2018 Penerbit UTM Press. All rights reserved.
date: 2018
publisher: Penerbit UTM Press
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048306222&doi=10.11113%2fjt.v80.11228&partnerID=40&md5=f94c5e2c30ef728856c74cb72fd6573e
id_number: 10.11113/jt.v80.11228
full_text_status: none
publication: Jurnal Teknologi
volume: 80
number: 4
pagerange: 1-16
refereed: TRUE
issn: 01279696
citation:   Samat, N.A.S.A. and Zabiri, H. and Kamaruddin, B.  (2018) The development and investigation analysis of an arx-based generalized likelihood ratio (GLR) stiction detection method.  Jurnal Teknologi, 80 (4).  pp. 1-16.  ISSN 01279696