A Fault Diagnosis Method for Analog Circuits Based on Improved TQWT and Inception Model

• Published in: Symmetry (Basel) Vol. 16; no. 6; p. 720
• Main Authors: Yuan, Xinjia, Yang, Siting, Wang, Wenmin, Sheng, Yunlong, Zhuang, Xuye, Yin, Jiancheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Algorithms; Analog circuits; Analysis; Artificial intelligence; Bandpass filters; Circuits; Classification; Electronic systems; Fault diagnosis; fault diagnosis of analog circuits; Feature extraction; GWO; High pass filters; inception model; Methods; Neural networks; Optimization; Parameter identification; Q factors; Support vector machines; SVM; TQWT; Wavelet transforms
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym16060720

A soft fault in an analog circuit is a symptom where the parameter range of a component exists symmetrically to the left and right of its nominal value and exceeds a specific range. The proposed method uses the Grey Wolf Optimization (GWO) optimized tunable Q-factor wavelet transform (TQWT) algorithm for feature refinement, the Inception model for feature extraction, and an SVM for fault diagnosis. First, the Q-factor is optimized to make it more compatible with the signal. Second, the signal is decomposed, and a single-branch reconstruction is performed using the TQWT to extract features adequately. Then, fault feature extraction is conducted using the Inception model to obtain multiscale features. Finally, a Support Vector Machine (SVM) is used to complete the entire fault diagnosis process. The proposed method is comprehensively evaluated using the Sallen–Key bandpass filter circuit and the four-op-amp biquad high-pass filter circuit widely used in electronic systems. The experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability.