Application of support vector machines plus to regression analysis for pressure-relief valves leaking

−− Carrying out regression analysis for gas leakage of pressure-relief valve (PRV) to get accurate leakage flow and changing trend of leakage will be helpful in assessing the reliability of PRV. Classic support vector regression (SVR) is an excellent regression model, and has been widely used in various fields. However, standard SVR model does regression only using leakage data without elements closely related to the leakage considered. In this paper a regression model based on support vector regression plus (SVR+) is put forward to perform leakage regression of PRV, in which particle swarm optimization (PSO) is used to select optimum parameters of SVR+, termed PSO_SVR+. The experimental results demonstrate that the proposed model taking the difference of inlet pressure and outlet pressure of PRV as hidden information can access a more favorable regression precision than SVR can provide. Meanwhile this article also investigates effects of PSO and Genetic Algorithm on the performance of regression model (SVR+ or SVR). Keywords−− Gas leakage, Regression modeling, Support vector regression, Hidden information, Particle swarm optimization