Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker

Based on empirical mode decomposition (EMD) method and support vector machine (SVM), a new method for the fault diagnosis of high voltage circuit breaker (CB) is proposed. The feature extraction method based on improved EMD energy entropy is detailedly analyzed and SVM is employed as a classifier. Radial basis function (RBF) is adopted as the kernel function of SVM and its kernel parameter γ and penalty parameter C must be carefully predetermined in establishing an efficient SVM model. Therefore, the purpose of this study is to develop a genetic algorithm-based SVM (GA-SVM) model that can determine the optimal parameters of SVM with the highest accuracy and generalization ability. The classification accuracy of this GA-SVM approach is tried by real dataset and compared with the SVM, which has randomly selected kernel function parameters. The experimental results indicate that the classification accuracy of this GA-SVM approach is more superior than that of the artificial neural network and the SVM which has constant and manually extracted parameters.