Research on oil-gas Pipeline Leakage Detection Method Based on Particle Swarm Optimization Algorithm Optimized Support Vector Machine

Abstract

In order to solve the problem of low accuracy in oil-gas pipeline leak detection, a pipeline leak detection method based on Particle Swarm Optimization (PSO) algorithm optimized Support Vector Machine (SVM) is introduced. This method uses PSO to solve the penalty factor ‘c’ and kernel function parameter ‘g’, and constructs the pipeline leakage detection model of SVM. We set up an experimental platform to collect negative pressure wave signals under different working conditions. After wavelet domain denoising and data preprocessing, four eigenvalues of Mean, Standard Deviation, Kurtosis and Skewness are extracted from the signals to form the eigenvector samples, which are taken as input of SVM, and four working conditions of normal, leakage, rise and fall are taken as output. Through experimental verification, the comprehensive performance of PSO-SVM algorithm is better than that of traditional SVM, Genetic Algorithm optimized SVM and grid search algorithm optimized SVM. The POS-SVM algorithm can be applied to the leak detection of oil-gas pipeline.