In 2020, [PERSON] used a cross-wavelet transform (XWT) to obtain the spectrum map of the faulty signal and then fed the spectrum map to a generative adversarial neural network (GAN) to achieve fault diagnosis of the CUT [11]. In 2021, [PERSON] obtained the spectrogram of the response of the CUT by short-time Fourier transform (STFT) and input it to ResNet to complete feature extraction and fault diagnosis of the CUT [12]. In 2021, [PERSON] used WT to preprocess the signal at the output of the CUT and input the preprocessed data into an improved deep convolutional neural network to complete the diagnosis of incipient faults in analog circuits [13]. According to the above methods, it can be concluded that the general process of analog circuit fault diagnosis is data preprocessing, feature extraction, and fault diagnosis. The experimental results of a large number of studies have shown that by preprocessing the response signal of the CUT, the speed and accuracy of fault diagnosis can be effectively improved. Common signal preprocessing methods include STFT, WT, WPT, EMD, EEMD, [14] and empirical wavelet transform (EWT) [15]. Although WT can be used continuously as a time function for signal localization analysis, it can only decompose the low-frequency part of the signal and the level of detail is insufficient. WPT can process the high- and low-frequency parts of the signal, but cannot perform data-driven frequency partitioning [16]. EMD can realise the adaptive decomposition of the original signal, but it lacks a theoretical basis and is prone to modal aliasing and end effects [17]. Although EEMD can eliminate mode aliasing and end effects, the algorithm is sophisticated and the time complexity is huge. The EWT proposed based on EMD and wavelet theory is not limited by the signal frequency band and can better eliminate mode aliasing and endpoint effects [15]. In fault diagnosis, support vector machines (SVMs) and artificial neural networks (ANNs) are the two most popular fault classification methods. SVM as a classifier requires additional feature analysis, extraction, or fusion, which can increase the complexity of analog circuit fault diagnosis. Compared with SVMs, ANNs can automatically learn the characteristics of the signal without manual intervention for feature analysis and processing. Inspired by the above papers, a novel analog circuit fault diagnosis method is proposed in this paper, which first applies a multi-input ResNet to analog circuit fault diagnosis. In addition, a square wave is used as the stimulus of the CUT, and then responses are preprocessed by the improved EWT. The multi-input ResNet is used to complete feature extraction and fault diagnosis on the basis of the components decomposed by the improved EWT. This paper is proposed mainly based on the following ideas. * The components of the response signal vary among different CUT fault types. The improved EWT can effectively extract each component of the response signal, which is conducive to reducing the input and layer number of the neural network and improving the efficiency of feature extraction and fault diagnosis;
* The multi-input ResNet can richly learn the characteristics of each component without extra feature analysis and processing, which is beneficial for reducing the complexity of analog circuit fault diagnosis and improving the accuracy. The rest of this paper is organized as follows. The second section introduces the analog circuit fault diagnosis method proposed in this paper. The third section verifies the effectiveness of the proposed method by the Sallen-Key bandpass filter circuit, CSTV filter circuit, and leap-frog low-pass filter circuit. Finally, the fourth section concludes and summarises the work of this paper.