An Analog Circuit Fault Diagnosis Method Based on ITQWT-MKL-SVM

Analog circuits as an important part of electronic devices, the normal operation is closely related to the stability of electronic devices. Therefore, to ensure that the analog circuits can work normally. An innovative analog circuit fault diagnosis method has been suggested, which combines Improved Tunable Q-factor Wavelet Transform (ITQWT) with Multi Kernel Learning Support Vector Machine (MKL-SVM). In this method, the improved TQWT and Inception model are employed to extract features, and the MKL-SVM is used for fault diagnosis. To verify the effectiveness of the proposed method, experiments are carried out using the Sallen-key band-pass filter circuit and the four-op-amp biquad high-pass filter circuit. The experimental findings indicate that this approach is successful in identifying issues within analog circuits.