## 5 Experiment Verification and Analysis
### Experiment on Datasets of Rolling Bearing in Case Western Reserve University
#### 5.1.4 Analyze the Process and Results  
Figure 8 shows the classification accuracy and loss function values of the training set and verification set after 50 rounds of iterations in 1 DCNN using the method proposed in this paper. Through the analysis of the accuracy and loss change curve, it can be seen that when the model is trained to the fourth time, the accuracy reaches a large value, and the accuracy fluctuates less in the subsequent training process, indicating that the network model training has been completed. The trained network model is tested with test samples, and the accuracy of the final test set is 99.67%. The experimental results show that the proposed model has few iterations, high convergence accuracy, and small loss, which shows that the network model designed by this method is reasonable. In order to show the recognition results of each category of the model in the test set more clearly, the confusion matrix is introduced to analyze the experimental results in detail, as shown in Figure 9, where 0-9 represent 10 states of the rolling bearing, respectively, and their representation labels are shown in Table 1. The X-axis represents the predicted category label of the fault, the Y-axis represents the real category label of the fault, the dark area on the diagonal of the figure represents the accuracy corresponding to each type of fault, 300 represents the number of test sets for each type of fault, and the values in other areas are the number of misclassification. As shown in the confusion matrix, the value of the corresponding position of real Category 4 and Predictive Category 1 is 1, which indicates that one test Category 4 is misclassified as Fault Category 1; that is, one 0.007-inch inner fault is misclassified to a rolling ball fault with a damage diameter of 0.007 inches. It can be seen from the diagram that except for the classification accuracy of test category 4 (IR007), the other nine fault types can be classified accurately, indicating that the combination of sparse representation and 1 DCNN proposed in this paper can  
Figure 8: (**a**) Accuracy of the SR-1 DCNN model; (**b**) Loss of the SR-1 DCNN model. Figure 7: Data augmentation with overlap. achieve satisfactory classification results in fault classification and diagnosis, and it has great feasibility. In the actual working conditions, the measured bearing vibration signals are often mixed with the interference of a large number of noise signals, so the robustness and generalization ability of a 1 DCNN model under different noise interferences are studied. The method was used to carry out 10 tests, and the accuracy of the test set was output, respectively. In the first experiment, Gaussian white noise with different SNRs was added to all 3000 test samples to simulate the influence of noise on the results of classification diagnosis under real working conditions. The definition of SNR is shown in Formula (6), and the trained 1 DCNN model is used to test the accuracy of the noisy-adding test set. \[SNR_{dB}=10 log_{10}\bigg{(}\frac{P_{signal}}{P_{noise}}\bigg{)} \tag{6}\]  
where \(P_{signal}\) represents the power of the original signal and \(P_{noise}\) represents the power of the noise signal. The smaller the SNR, the greater the noise interference. According to the data obtained from 10 experiments in Figure 10, the average accuracy of the model based on this method for rolling bearing fault diagnosis is 99.82%, with the highest accuracy of 99.96% and the lowest accuracy of 99.56%. The results of 10 experiments show that the model based on the method adopted in this paper has good robustness in fault classification and identification of rolling bearings. Figure 10: Accuracy of ten experiments (test set of CWRU). Figure 9: Confusion matrix of the SR-1 DCNN model (test set of CWRU). The classification accuracy of the test set under different SNRs is shown in Figure 11. After the noise interference of different SNRs is added to the test sample, the model can still quickly learn the characteristics of the data.