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. S. Kalaivani (B) Electronics and Instrumentation Engineering, Muthayammal Engineering College, Namakkal, India e-mail: [email protected] T. Aravind Computer Science Engineering, Muthayammal Engineering College, Namakkal, India e-mail: [email protected] D. Yuvaraj Instrumentation and Control Engineering, Tamilnadu College of Engineering, Coimbatore, India e-mail: [email protected] B. V. Babu et al. (eds.), Proceedings of the Second International Conference on Soft Computing 13 for Problem Solving (SocProS 2012), December 28–30, 2012, Advances in Intelligent Systems and Computing 236, DOI: 10.1007/978-81-322-1602-5_2, © Springer India 2014 14 S. Kalaivani et al.