Analog Circuit Fault Diagnosis Based on Wavelet Kernel Support Vector Machine

Analog circuit fault diagnosis can be regarded as the pattern recognition issue and addressed by machine learning theory. As compared with neural networks, support Vector Machine (SVM) is based on statistical learning theory, which has advantages of better classification ability and generalization performance. The marr wavelet kernel is proposed and the existence is proven by theoretic analysis and demonstration. Based on this, a novel analog circuit fault diagnosis method which is called wavelet kernel support vector machine is proposed in the paper. Using principal component analysis (PCA) as a tool for extracting fault features, the WSVM is then applied to the analog circuit fault diagnosis. The effectiveness of the proposed method is verified by the experimental results.