An algorithm of soft fault diagnosis for analog circuit based on the optimized SVM by GA

Abstract

A soft fault diagnosis method for analog circuits based on support vector machine (SVM)is developed in this paper. SVM is a novel machine learning method based on the statistical learning theory, which is a powerful tool for solving the problem with small sampling, nonlinearity and high dimension. The multi-classification SVM methods including one versus rest, one versus one, and decision directed acyclic graph (DDAG) has been applied to many areas. Some researchers have used it in fault diagnosis of analog circuit. The selection of SVM parameters has an important influence on the classification accuracy of SVM. However, it is very difficult to select appropriate SVM parameters. In this study, support vector machine with genetic algorithm (GA-SVM) is applied to fault diagnosis, in which genetic algorithm (GA) is used to select appropriate parameters of SVM. The experimental results of a negative feedback amplifier circuit indicate that the GA-SVM method can achieve higher diagnostic accuracy than normal SVM classifier and artificial neural network.