## 4 Experimental Analysis
### Experimental Analyses
#### 4.3.4 Comparison with Existing Popular Models  
In this section, to validate the superiority of MARN, several relevant models using 2D images as input data are compared. [PERSON] et al. and [PERSON] et al. used an algorithm combining MTF with residual network [23; 24]; [PERSON] et al. used a new MTF-CNN method for fault diagnosis in complex working conditions [25]; [PERSON] et al. proposed an improved model MTF-SE-ResNet for the diagnosis of bearings under compound faults [26]; [PERSON] et al. proposed a GADF with an improved channel attention model (FcaNet) [17]; [PERSON] et al. proposed a RP and MobileNet-v3 for fault diagnosis of variable speed bearings [27]. To ensure fairness in model training, the same dataset is used as input for the different networks, with the same training settings for each model. To ensure that the models converged sufficiently, 200 rounds of iterations of each model are performed. The balanced dataset \(\text{D}_{\text{A}}\) and unbalanced dataset \(\text{D}_{\text{B}}\) are taken as examples, respectively, and the corresponding training performance curves are shown in Figure 15. Table 5 shows the maximum validation accuracy of different methods on the four datasets. As can be seen in Figure 15, all models have higher loss and training fluctuations on the unbalanced dataset than on the balanced dataset, which is due to the fact that a smaller amount of data has an indispensable effect on the models in terms of deep learning. Although the lightweight model MobileNet-v3 converged the fastest, the highest validation accuracy is only about 89% and has the highest training loss; this is due to the intrinsic structure of the model resulting in inadequate feature extraction. The shallow model NCNN tends to converge steadily during training, but converges too slowly, requires more iterations, and has higher training losses. Although the training loss of SE-ResNet and FcaNet is close to the proposed method, the training is not stable enough, with occasional sharp fluctuations during the training process. ResNet50 uses a deeper structure for feature extraction, and the loss is instead higher, thus illustrating that increasing the depth of the model does not necessarily result in effective feature extraction. Therefore, the proposed method can keep the loss low and the fluctuation stable regardless of the dataset. From Table 5, it can be seen that the highest validation accuracy of FcaNet on unbalanced datasets \(\text{D}_{\text{B}}\) and \(\text{D}_{\text{D}}\) is the same as the proposed method; and the other methods are not as good as the present model on different datasets. Therefore, the proposed method can extract more features. Figure 14: t-SNE dimensionality reduction: (a) \(\text{D}_{\text{A}}\); (b) \(\text{D}_{\text{B}}\); (c) \(\text{D}_{\text{C}}\); (d) \(\text{D}_{\text{D}}\). In the field of DL, evaluation metrics are the criteria for determining the performance of a model, so it is important to compare the classification metrics of different models. Accuracy \(A_{c}\), precision \(P_{r}\), recall \(R_{e}\), and \(F1\) score are all criteria for determining the performance of the model, determined by Equations (7)-(10). \[A_{c}=\frac{TP+TN}{TP+FP+TN+FN} \tag{7}\]  
\[P_{r}=\frac{TP}{TP+FP} \tag{8}\]  
\[R_{e}=\frac{TP}{TP+FN} \tag{9}\]  
\[F1=\frac{2\times P_{r}\times R_{e}}{P_{r}+R_{e}} \tag{10}\]  
where \(TP\) and \(TN\) are the number of correct predictions in 10 categories; \(FP\) and \(FN\) are the numbers of incorrect predictions in 10 categories. Figure 15: Training performance curves comparing different models: (**a**) \(\rm D_{A}\) validation accuracy; (**b**) \(\rm D_{A}\) training loss; (**c**) \(\rm D_{B}\) validation accuracy; (**d**) \(\rm D_{B}\) training loss.