## 5 Experimental Results and Discussion
### Experiment 1
#### 5.1.2 Performance Comparison and Discussion
In the literature [18], it can be seen that the fault characteristics of the data after spectral analysis were more prominent, so that the CNN model based on time-frequency analysis achieved a significant improvement in the recognition rate. In [31], it could be seen that the fault characteristics of the data after spectral analysis were more obvious; thus, the CNN model based on time-frequency analysis had a significant improvement in the recognition rate. The STFT-CNN model [7] achieved an accuracy rate of 99.87%. However, the two-dimensional spectral analysis data would cause some unsatisfactory results, such as the structural redundancy of the network, slow convergence speed, and long training time. In addition, an appropriate selection of a transformation method would result in the loss of information. The 1D-CNN model has been proven to be a good solution to these problems. In [23], the authors used the original signal as the input data of the 1D-CNN model, and a good classification effect was obtained, while the number of iterations was reduced. However, the original signal had a great sensitivity to the noise, which made it difficult for the 1D-CNN model to adapt to the complex and changeable noise interference in the actual production process. The model proposed in this paper represents a good combination of the advantages of the above two models, the original signal as the first layer of input data, while adding a number of spectral analysis data input layer, so in the case of short training time, it has a high recognition accuracy and robustness. \begin{table}
\begin{tabular}{c c c} \hline
**Algorithm Model** & **Number of Epochs** & **Accuracy (\%)** \\ \hline FFT-SVM & - & 84.20 \\ Wavelet-ANN & - & 88.54 \\ EMD-ANN & - & 96.24 \\ DWT-CNN [39] & 60 & 99.80 \\ PSO-CNN [31] & 20 & 92.84 \\ WP-PSO-CNN [31] & 20 & 99.71 \\ STFT-CNN [7] & 16 & 99.87 \\ ACNN-FD [42] & 12 & 99.40 \\ MDI-CNN & 5 & 99.96 \\ \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy of different models. Figure 8: Feature visualization of different samples. (**a**) The raw samples. (**b**) Fully connected layer sample. Next, the robustness of the proposed model was validated. The test set B was mixed with different SNR noise and then fed into the MDI-CNN model trained with the training set A, and the obtained classification results are shown in Figure 9. In Figure 9, it can be seen that with the increase in the noise signal strength, the recognition accuracy of all the models decreased, among which the classification accuracy of the 1D-CNN and 2D-CNN models significantly decreased when the fault signals mixed with noise were identified. The recognition rate of the proposed model was better than that of the other two models. The incremental training was carried out by entering the training set B containing the noise into the MDI-CNN trained by the training set A to extract the fault characteristics that were not sensitive to the noise. After ten tests, the identification results of the three models are shown in Figure 10.  
The comparison showed that the proposed model achieved a high recognition rate under the interference of high-intensity noise, which was better compared with the other two models. The recognition accuracy of the proposed model was further improved by the incremental training, which verified that the fault diagnosis model based on the MIL-CNN had high robustness and generalization ability. Figure 10: Testing the accuracy of MDI-CNN after incremental training. Figure 9: Testing diagnosis results of different models under different noise intensity.