## 5 Experiment Verification and Analysis
### Dataset Experiment of Worm Gear Reducer
#### 5.2.2 Analysis Process and Results  
Figure 15 show that when the number of trainings is about five times, the network model has tended to be stable, and the accuracy of the test set has reached 99.83%. The results show that the network model based on this method is also applicable to the experimental dataset of the worm gear reducer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fault Location** & **Normal** & **Pitting** & **Spalling** & **Breaking** \\ \hline Category labels & 0 & 1 & 2 & 3 \\ Sample length & 2048 & 2048 & 2048 & 2048 \\ Load (Nm) & 0.6 & 0.6 & 0.6 & 0.6 \\ Training sample & 600 & 600 & 600 & 600 \\ Validation sample & 100 & 100 & 100 & 100 \\ Testing sample & 300 & 300 & 300 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of the worm gear dataset and sample partition.  
Figure 13: Worm gear fault test rig.  
As shown in Figure 16, the multi-class confusion matrix of the result is obtained for the first time by this method. The results show that the overall diagnostic accuracy of the four categories was 99.83%, and the error rate is 0.17%. Among them, two samples in category 3 are misclassified to category 0; that is, two samples of broken teeth are misclassified to normal faults, and there is only one misclassification, and the classification effect is satisfactory.  
In order to evaluate the fault identification ability of the proposed model, the network model is used to carry out 10 fault classification experiments on the dataset, and the test results are shown in Figure 17. The average accuracy of 10 experiments is 99.76%, the highest accuracy is 99.93%, and the lowest accuracy is 99.56%. The results show that this method has more reliable performance.  
Figure 14: Original vibration signal time-domain waveforms of worm gear.  
Figure 15: (**a**) Accuracy of the SR\(-\)1 DCNN model; (**b**) Loss of the SR\(-\)1 DCNN model.  
As can be seen in Figure 18, the accuracy of the SR-1 DCNN fault diagnosis model proposed in this paper for worm gear faults under low SNR is significantly higher than that of the model that classifies the original signals directly. The classification accuracy of the model is 35.6% when SNR = \(-4\) dB. When the signal-to-noise ratio is changed to 6 dB, the accuracy can reach more than 90%. It can still maintain a high accuracy in the presence of noise, which proves the effectiveness of the model for worm gear fault diagnosis in noisy environment.  
Figure 16: Confusion matrix of the SR-1 DCNN model (test set of Worm Gear Reducer).  
Figure 17: Accuracy of ten experiments (test set of Worm Gear Reducer).  
Figure 18: Accuracy of test set under different SNR (test set of Worm Gear Reducer).  
Figure 19 shows the classification performance comparison results of different methods. In this experiment, four types of worm gear fault data composed of 0 Nm and 6 Nm under four fault states are used. Compared with reference [48] SFDSC and reference [49] SISC, experiments were carried out under two loads of SISC, SFDSC0 Nm and 6 Nm, each with four types of data, in which the diagnosis accuracy of the 0 Nm load was higher than that of the 6 Nm load, reaching 90.56% and 96.67% respectively, while the classification accuracy of this method reached 99.76%, which was higher than that of the above two models. The results show that the method proposed can effectively extract fault features and has high classification accuracy.