## 3 Experimental Validation
### Experimental Results and Analysis  
The test sets of the three CUTs are input to the optimal multi-input ResNet model corresponding to the CUTs to obtain the confusion matrix shown in Figure 14. Figure 14a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Unit \& Layer** & **Convolution Size** & **Stride** & **BN** & **Activation Function** \\ \hline \multirow{8}{*}{Single ResNet unit} & convolution block & 5\({}^{\star}\) 16 & 2 & Y & Relu \\  & average pooling layer & 3 & 2 & N & N \\  & 3\({}^{\star}\) 16 & 2 & Y & Relu \\  & ResNet block1 & 3\({}^{\star}\) 16 & 1 & Y & Relu \\  & 3\({}^{\star}\) 32 & 2 & Y & Relu \\  & ResNet block2 & 3\({}^{\star}\) 32 & 1 & Y & Relu \\  & ResNet block3 & 3\({}^{\star}\) 64 & 2 & Y & Relu \\  & 3\({}^{\star}\) 64 & 1 & Y & Relu \\  & ResNet block4 & 3\({}^{\star}\) 128 & 2 & [PERSON] & [PERSON] \\  & average pooling layer & 7 & 1 & N & N \\ \hline \multicolumn{2}{c}{Concat layer} & 128 \({}^{\star}\) 5 & - & - & - \\ \hline FC layer & N\_class & - & - & Softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Details of the multi-input ResNet. Figure 12: Multi-input ResNet model training process. shows that the various faults of the Sallen-Key bandpass filter circuit can be distinguished well and there is no confusion or difficulty in distinguishing between different fault types. Figure 14c shows that the confusion between F4 and F10 is serious. Two samples with fault code F9 were diagnosed as the fault with fault code F3, a sample with fault code F13 was diagnosed as the fault with fault code F17, two samples with fault code F14 were diagnosed as the fault with fault code F12 and F18, respectively, and a sample with fault code F18 was diagnosed as the fault with fault code F6. In conclusion, the fault diagnosis result of the leap-frog filter circuit is worse than the other two circuits, which also reflects that the more complex the circuit is, the more confusion is generated between different faults and the fault diagnosis is more difficult. Figure 14: _Cont._  
Figure 13: Dimensionality reduction visualisation of CSTV filter circuit responses based on t-SNE. (**a**) Visualisation of the original response signal; and (**b**) visualisation of features extracted by _Modeloptima_. To test the effectiveness of the analog circuit fault diagnosis method proposed in this paper, the proposed method is compared with the results in [8; 10; 13]. The fault diagnosis results of these three CUTs are shown in Tables 5-7. A sinusoidal sweep signal was used as the stimulus of the CUT in [8], and a single pulse signal was used as the excitation signal of the CUT in [10; 13]. Without preprocessing the CUT response signal, the extracted features are directly input into the neural network to complete the fault diagnosis in [8]. EEMD was used to preprocess signals, and then the extracted features were input into ELM to  
Figure 14: Confusion matrix. (**a**) Confusion matrix of the Sallen–Key bandpass filter circuit response; (**b**) confusion matrix of the CSTV filter circuit response; and (**c**) confusion matrix of the leap-frog low-pass filter circuit response. complete fault diagnosis in [10]. WT is used to preprocess signals, and the preprocessed data are input into the convolutional neural network model to complete feature extraction and fault diagnosis in [13]. In Table 5, the average diagnostic accuracy of [8] is the lowest. In Table 6, the average diagnostic accuracy of [8] is slightly higher than that of [10]. In Table 7, the average diagnostic accuracy of [8] is only higher than that of [10]. It is clear that preprocessing the CUT response signal helps to improve the accuracy of fault diagnosis. In Tables 5 and 7, the average diagnostic accuracy of [8,10] is lower than that of [13], the proposed method and the traditional EWT combined with multi-input ResNet. Only in Table 6 is the average diagnostic accuracy of [8,10] slightly higher than that of [13].