Novel analog circuit fault diagnosis method based on SVM of learning using privileged information
基于融合特权信息支持向量机的模拟电路故障诊断新方法

This paper proposed a novel fault diagnosis method based on SVM of learning using privileged information (LUPI-SVM),aiming at solving the problem of correctly identifying fault classes in analog circuit fault diagnosis.Firstly, the fault feature vectors were extracted by PCA(principal component analysis) feature extraction method. Then, after training the LUPI-SVM by faulty feature vectors, the LUPI-SVM model of the circuit fault diagnosis system was built. Finally, input the test samples' feature vectors into the trained LUPI-SVM model to identify the different fault cases. The simulation results for analog and mixed-signal test benchmark Sallen-Key filter circuits demonstrate that the proposed method improves classification ability. It correctly classifies not only the single hard fault classes with a highly average classification success rate more than 99%, but also the multiple fault classes.The method develops a new direction for the fault diagnosis of analog circuit.