Electronic circuit fault diagnosis methods based on improved Support Vector Machines

In nowadays, fault diagnosis method for analog circuit based on support vector machines, has become a hot topic in research field of fault diagnosis. However, in practical application of this method, the imbalanced problem occurred in fault sample dataset has greatly influenced its effectiveness. To remedy this problem, this paper proposed an improved Support Vector Machines method based on biased empirical feature mapping. In the new method, biased discriminant analysis was applied in empirical feature space, to make all normal samples far away from center of fault samples, so that the overall fault diagnosis ability can be improved. Through theoretical analysis and empirical study on actual electronic circuit fault diagnosis problem, we show that our method augments the diagnosis accuracy rate effectively.