Research on fault diagnosis method of analog circuit based on improved VMD and SVM

The integration and complexity of analog circuits are getting higher and higher, and it is becoming more and more difficult to extract the characteristic information of its response. In order to solve the problem of extracting fault information, an algorithm combining variational modal decomposition (VMD) and compound multi-scale permutation entropy (CMPE) is proposed to construct a fault feature vector, and the support vector machine (SSA-SVM) optimized by the sparrow search algorithm is used to complete the fault classification. Firstly, the original signal at the time of failure is collected by the PSPICE software, and processed by VMD into multiple groups of IMF components containing the original signal characteristics. Secondly, the CMPE values of the first 3 IMF components are calculated, and the normalized processing is used as the fault feature vector. Finally, in the classification In-device training and testing. The simulation test shows that the final diagnosis accuracy rate of this scheme can reach 99.67%. Compared with other schemes, it can effectively improve the accuracy of fault diagnosis, and it is a feasible analog circuit fault diagnosis idea.