# Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network
###### Abstract  
Analog circuits play an important role in modern electronic systems. Aiming to accurately diagnose the faults of analog circuits, this paper proposes a novel variant of a convolutional neural network, namely, a multi-scale convolutional neural network with a selective kernel (MSCNN-SK). In MSCNN-SK, a multi-scale average difference layer is developed to compute multi-scale average difference sequences, and then these sequences are taken as the input of the model, which enables it to mine potential fault characteristics. In addition, a dynamic convolution kernel selection mechanism is introduced to adaptively adjust the receptive field, so that the feature extraction ability of MSCNN-SK is enhanced. Based on two well-known fault diagnosis circuits, comparison experiments are conducted, and experimental results show that our proposed method achieves higher performance.  
analog circuit; fault diagnosis; convolutional neural network; feature extraction +
Footnote †: journal:  
[PERSON] et al. [13] developed a method to extract features adaptively from the raw time series signals and automatically classifies the faults of analog circuit.  
As a typical DL model, a convolutional neural network (CNN) has received continuous attention from researchers. The CNN model benefits in local weight sharing and the local receptive field, so as to effectively mine high-level feature representations from the input [14; 15]. In several recent studies, CNN-based methods were introduced for analog circuit fault diagnosis and achieved remarkable results. [PERSON] et al. [16] proposed a one-dimensional convolutional neural network (1 DCNN) to conduct analog circuit fault diagnosis, which used raw signals as the input. [PERSON] et al. [17] developed a CNN-based approach for analog circuit fault diagnosis, and the output signals in different fault states are directly input into CNN. However, these studies still have the following two weaknesses: (1) they directly use the raw output signals as the input of the CNN model, which can only extract the numerical characteristics. (2) The receptive field of the CNN model is fixed, which limits its feature extraction ability.  
To address these weaknesses, this paper proposes a multi-scale convolutional neural network with a selective kernel (MSCNN-SK). This network integrates a multi-scale average difference layer, which could provide additional helpful information for the network by computing multi-scale average difference sequence. Moreover, a dynamic convolution kernel selection mechanism is introduced into the MSCNN-SK to adaptively adjust its receptive field. Based on two commonly used fault diagnosis circuits, i.e., a Sallen-Key band-pass filter circuit and a Four-opamp biquad high-pass filter circuit, the effectiveness of the proposed MSCNN-SK is verified. Experimental results indicate that our MSCNN-SK outperforms several compared fault diagnosis methods.  
The rest of this paper is organized as follows: Section 2 briefly introduces the basic compositions of CNN and describes two useful strategies in detail. Section 3 illustrates the proposed MSCNN-SK. The effectiveness of the proposed method is evaluated in Section 4. Finally, Section 5 provides conclusions and future directions.