A Single Curve Piecewise Fitting Method for Detecting Valve Stiction and Quantification in Oscillating Control Loops
Stiction is one of the most common problems in the spring-diaphragm type control valves, which are widely used in the process industry. In this paper, a procedure for single curve piecewise fitting stiction detection method and quantifying valve stiction in control loops based on ant colony optimization has been proposed. The single curve piecewise fitting method of detecting valve stiction is based on the qualitative analysis of the control signals. The basic idea of this method is to fit two different functions, triangular wave and sinusoidal wave, to the controller output data. The calculation of stiction index (SI) is introduced based on the proposed method to facilitate the automatic detection of stiction. A better fit to a triangular wave indicates valve stiction, while a better fit to a sinusoidal wave indicates nonstiction. This method is time saving and easiest method for detecting the stiction. Ant colony optimization (ACO), an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model estimated using ant colony optimization, from the input–output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.
KeywordsControl valve stiction Stenman model Single curve piecewise fitting Ant colony optimization
- 4.He, Q.p., Pottmann.: Detection of valve stiction using curve fitting. Internal Report, Process Dynamics and Control. Dupont Engineering, (2003).Google Scholar
- 8.Toliyat, H.A., Levi, E., Raina, M.: A review of RFO induction motor parameter estimation techniques. IEEE Trans. Ene. Conv. 18, 271–283 (June 2003)Google Scholar
- 9.Sivagamasundari, S., Sivakumar, D.: Estimation of valve stiction using particle swarm optimization. J. Sens. Transducers 129, 149–162 (2011)Google Scholar