A Novel Fault Diagnosis Method for Analog Circuits Based on Multi-Input Deep Residual Networks with an Improved Empirical Wavelet Transform

• Published in: Applied sciences Vol. 12; no. 3; p. 1675
• Main Authors: Liu, Zhen, Liu, Xuemei, Xie, Songlin, Wang, Junhai, Zhou, Xiuyun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Accuracy; Algorithms; analog circuit; Artificial intelligence; circuit under test (CUT); Circuits; empirical wavelet transform (EWT); Fault diagnosis; Methods; multi-input deep residual network (ResNet); Neural networks; Simulation; Standard deviation; Support vector machines; Wavelet transforms
• DOI: 10.3390/app12031675
• ISSN: 2076-3417

Analog circuits play an essential role in electronic systems. To strengthen the reliability of sophisticated electronic circuits, this paper proposes a novel analog circuit fault diagnosis method. Compared with traditional fault diagnosis, the fault diagnosis process in this paper uses a square wave as the stimulus of the circuit under test (CUT), which is beneficial for obtaining the response of the CUT with rich time and frequency domain information. The improved empirical wavelet transform (EWT), which can more accurately extract the amplitude modulated–frequency modulated (AM-FM) components, is used to preprocess the original response. Finally, based on the preprocessed data, a multi-input deep residual network (ResNet) is constructed for fault feature extraction and fault classification. The multi-input ResNet is a powerful approach for learning the fault characteristics of the CUT under different faults by learning the characteristics of the AM-FM components. The effectiveness of the method proposed in this paper is verified by comparing different fault diagnosis methods.