Investigation on Statistical-based Detection Techniques For Control Valve Stiction Detection

• Main Author: Abdullah, Khadijah
• Format: Final Year Project
• Language: English
• Published: IRC 2015
• Subjects: TP Chemical technology
• Online Access: http://utpedia.utp.edu.my/16303/1/Dissertation%20Khadijah%2015250.pdf; http://utpedia.utp.edu.my/16303/

Control valve stiction is one of the main causes that can affect the performance of a control loop. As the final control element, it can cause disruptions towards the operations especially on the plant production of oil and gas industry. An initiative had been made in 1989 where a stiction detection method is first developed to detect stiction in control valve. Since then, many methods are produced and redeveloped but only few uses the statistical-based methods. Hence, this project will cover statistical-based methods which had once been used for fault detection and will be tested for the effectiveness in detecting control valve stiction. Two case studies are chosen which are simulation case study involving stiction and non-stiction conditions namely, well-tuned controller (Base case), tightly-tuned controller (Case 1), presence of disturbances controller (Case 2) and presence of stiction controller (Case 3). Another case study is the real industrial data from a chemical plant. The process output (pv) and controller output (op) for each case are generated based on nonlinear principle component analysis (NLPCA) method. Two statistical-based methods are chosen to be tested which are generalized likelihood ratio (GLR) test as well as error testing method. Based on the results of testing for method 1, there are some limitations for GLR test method to detect between the presence of stiction and the presence of disturbances in the system. However, for method 2, it can be seen that the error testing method focusing at the root mean squared error (RMSE) calculation is an effective tool and method to detect stiction and manage to differentiate stiction and non-stiction system for the simulation and real industrial case studies chosen.