# Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN
## 4 Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN
### Test Results and Analysis  
(1) Accuracy at different levels of noise  
In order to simulate various noise interferences in the actual operation of the nuclear power gearbox and test the diagnostic accuracy of the model under different levels of noise, five kinds of noise with different SNR are set, and the accuracy of the test set under different SNRs and different RSBUs is shown in Figure 13. When RSBUs is equal to three, the fault diagnosis accuracy is the highest, which is consistent with the previous results. Therefore, RSBUs equal to three is determined as the fixed parameter of DC-DRSN. It is not difficult to see that with the increase in the SNR, the accuracy of diagnosis also increases, and the diagnostic accuracy of the test set is still as high as 97.22% when the SNR is 4 dB. In view of the fact that the SNR in gearbox fault diagnosis studies is often between 0 and 10 dB to simulate the noise level in the actual industrial environment [30], and 4 dB is in the middle of this range, which is a reasonable and representative value, and is helpful to test the robustness and reliability of the fault diagnosis method, therefore, under the SNR 4 dB, DC-DRSN is used to train and classify the nuclear power planetary gearbox dataset, and the accuracy changes in the obtained training set and the validation set are shown in Figure 14. It can be seen that when the number of iterations is less than 50, the accuracy of the training set and the validation set increases but there is oscillation, and the diagnostic performance of the model is improving but the stability needs to be improved. When the number of iterations is more than 50, the training set gradually stabilizes, and the accuracy is close to 100%, and the oscillation of the validation set is greatly reduced compared with before, and the accuracy reaches 99%  
The confusion matrix and the T-SNE visualization representation of the classification results are shown in Figures 15 and 16, respectively. It is not difficult to find that label 2 has the best sample classification effect, with an accuracy rate of 100%. Label 0 and label 4 have six samples misclassified with each other, five samples from label 3 are classified into label 1, and eight samples from label 1 are misclassified. The results show that the change of gear crack type and load has a great influence on the classification accuracy, and most of the samples are correctly classified. Although there are a few sample errors, the minimum accuracy is still 95.56%, which verifies the effectiveness of DC-DRSN classification for different working conditions. The T-SNE visualization also shows that the distribution interval of each type of fault is regular and orderly, and the spacing of similar faults is very small, which further verifies the superiority of the proposed method. Figure 14: DB-DRSN training process with SNR 4 dB. Figure 15: Confusion matrix categorization of the experiment. (2) Comparison of different image conversion methods  
The collected planetary gearbox vibration signals are converted into two-dimensional time-frequency maps of 112 \(\times\) 112 pixels by STFT, WT, GASF, and GST, and then the time-frequency maps are input into the upper channel of the DC-DRSN model for diagnosis. When the SNR is equal to 4 dB, the fault diagnosis accuracy of each method is shown in Table 7. It can be seen that the GST image conversion method has the lowest diagnostic accuracy of 95.74%, while the STFT is as high as 97.22%. This further verifies that the STFT conversion image is used as the input of the DC-DRSN upper channel, and the time and frequency information of the signal are retained through local analysis of the signal in time and frequency. Therefore, the network can better capture the transient changes, periodicity, and frequency changes of the signal, so as to improve the accuracy of model diagnosis. (3) Comparison of DC-DRSN with other deep learning models  
Similarly, the proposed method DC-DRSN is compared with DRSN, VGG-16, GoogleNet, and Alexnet-18.