Fault diagnosis of analog circuit based on a second map SVDD

Abstract

To improve the diagnosis accuracy of analog circuit, this paper presents a second map support vector data description (SM-SVDD) method, which uses an anomalous and close surface instead of a hypesphere to describe the target data. The fault classifier is constructed by a SM SVDD algorithm to realize analog circuit fault diagnosis. Experimental results on two typical circuits confirm that the proposed method is effective in analog circuit fault diagnosis with good accuracy, and the performance surpasses other intelligent diagnosis methods, such as back propagation neural network, support vector machine and the normal SVDD. The developed method can be applied to other multi-classifier in electronic applications.