Fault Diagnosis Approach for Analog Circuits Using Minimum Spanning Tree SVM

A fault diagnosis approach for analog circuits based on minimum spanning tree (MST) support vector machine (SVM) is proposed. Fault features of analog circuits are extracted by wavelet analysis method. By taking separability measure of fault classes as weights of edges in feature space, the MST is generated and the sub-class separation for fault groups with clustering property is achieved. The node distribution of fault decision tree is then optimized. Hierarchical multi-class SVMs with large margins are constituted according to the structure of MST, which can effectively improve the fault diagnosis accuracy of analog circuits. The presented approach simplifies the structure of multiclass SVMs. Case study shows that our approach achieves more precision and higher efficiency comparing with other conventional SVM methods in analog circuit fault diagnosis.