Objective:
In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on
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 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. Afterwards, 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.
Stochastic change detection based on an active fault diagnosis approach
