An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based Fractal Analysis and Multiple Kernel SVM

Objectives: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on the basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then the wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterward, features are divided into training data and testing data. MKSVM, with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm, is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.