## 3 Experimental Validation
### Multi-Input ResNet Model Training  
The structure of the multi-input ResNet model proposed in this paper is introduced in Section 3. In the experiment, the detailed information of the convolution block, residual block, and average pooling layer of a single ResNet unit, as well as the subsequent concat layer and FC layer, are shown in Table 4, where N_class denotes the fault number of a CUT. The number of ResNet units is consistent with the number of components of the CUT response signal; therefore, the multi-input ResNet in the experiment possesses 5 ResNet units with the same structure. The training epoch of the multi-input ResNet is set to 100, and the training loss and validation loss are calculated by the cross-entropy loss function. The Adam optimiser is selected for ResNet parameter optimisation, and the learning rate of the optimiser is set to \(1\times 10^{-2}\).  
The training set is used to train the multi-input ResNet. For each epoch of training, the generalisation ability and classification ability of the multi-input ResNet model trained in this epoch are tested by the validation set. The training and verification losses are calculated by the cross entropy loss function. If the multi-input ResNet model trained by the training set in epoch \(p\) is \(ResNet-Model_{p},p=1\cdots 100\) and the classification ability and generalisation ability of \(ResNet-Model_{p}\) are verified by the verification set, then the verification accuracy of the model in epoch \(p\) is \(Vacc_{p}\). After 100 epochs of training and verification, we obtain \(Model=[ResNet-Model_{1},\cdots,ResNet-Model_{p},\cdots,ResNet-Model_{100}]\) and \(Vacc=[Vacc_{1},\cdots,Vacc_{p},\cdots,Vacc_{100}]\). The curves of training loss, training accuracy, verification accuracy, and verification loss in the training process of the multi-input ResNet model based on CSTV filter circuit response signals are shown in Figure 12. When the train  
Figure 11: AM-FM components of the responses of fault-free CUT based on improved EWT: **(a)** components of the Sallen–Key bandpass filter circuit response; **(b)** components of the CSTV filter circuit response; and **(c)** components of the leap-frog low-pass filter circuit response.  
ing loss converges, the multi-input ResNet model corresponding to the highest verification accuracy has the best generalisation ability and classification ability, and the diagnosis effect is the best. If the index of the maximum value of \(Vacc\) is \(ind\), the best multi-input ResNet model through 100 epochs of training is \(Model_{optima}=Model[ind]\).  
Based on the t-SNE, the original response signal of the CSTV filter circuit and the features extracted by \(Model_{optima}\) are dimensionalised separately. The visualisation results after dimensionality reduction are shown in Figure 13. From Figure 13a, it is obvious that the discrimination of the original response signals of the CSTV filter circuit is poor and multiple fault types merge with each other and thus are difficult to distinguish. It can be seen from Figure 13b that after feature extraction with \(Model_{optima}\), features of the same fault type present good aggregation, while those of different fault types present large spacing and good differentiation. These findings verify the effectiveness of the multi-input ResNet's fault feature extraction.