Analog Circuit Fault Diagnosis Based on Adaptive Gaussian Deep Belief Network

¹ Department of Electronic and Optical Engineering, Army Engineering University of PLA, Shijiazhuang, China
² School of mechanical engineering, Shijiazhuang Tiedao University, Shijiazhuang, China

Corresponding author: f_author, 791191357@qq.com

Abstract

Aiming at the problem that the traditional intelligent fault diagnosis method is overly dependent on feature extraction and the lack of generalization ability, deep belief network is proposed for the fault diagnosis of the analog circuit; Then, by analyzing the deficiency of deep belief network application, a Gaussian deep belief network based on adaptive learning rate is proposed. The automatic adjustment learning step is adopted to further improve fault diagnosis efficiency and diagnosis accuracy; Finally, particle swarm support vector machine to extract the fault characteristics to identify. The simulation results of circuit fault diagnosis show that the algorithm has faster convergence speed and higher fault diagnosis accuracy.