An analog circuit fault diagnosis method using improved sparrow search algorithm and support vector machine.

Abstract

In analog circuits, component tolerances and circuit nonlinearity pose obstacles to fault diagnosis. To solve this problem, a soft fault diagnosis method based on Sparrow Search Algorithm (SSA) and Support Vector Machine (SVM) is used. In this study, ISSA is obtained by optimization using four strategies for SSA deficiency. Twenty-three benchmark functions are used for optimization experiments, and ISSA converges faster, more accurately, and with better robustness than other swarm intelligence algorithms. Finally, ISSA is used to optimize the SVM parameters and establish the ISSA-SVM fault diagnosis model. In the Sallen-key test circuit diagnosis experiments, the correct fault diagnosis rates of SSA-SVM and ISSA-SVM are 97.41% and 98.15%, respectively. The results show that the optimized ISSA-SVM model has a good analog circuit fault diagnosis with an increase in diagnostic accuracy.