## 5 Results and Discussion  
The specific experimental environment for completing the data dimensionality reduction and fault diagnosis stages on the Windows 10 operating system is shown in Table 3.  
After completing steps 1-5 of the fault diagnosis steps, the results are shown in Figure 5.  
Subsequently, the 72 features, GLCM_0, GLCM_1,... and GLCM_71, were extracted using GLCM. Two fault diagnosis models, RFR-GA-BLS and CFR-GA-BLS, are applied to diagnose the faults, respectively. The RFR method and correlation feature ranking (CFR) are applied to rank these features, respectively, where the CFR method is the comparison scheme set up, based on the correlation of the features. It is obvious that these two feature ranking methods have a very different order for the ranked features. After sorting, the datasets were divided in a ratio of 9:1. Totals of 90 of each class were used as the training set and of 10 as the test set. Additionally, then the GA-BLS model is set up in turn. For each round of feature increase, the training accuracy and test accuracy are saved, and the results are shown in Figure 6.  
Figure 6a indicates the running results of CFR-GA-BLS. The first 55 features satisfy the algorithm conditions, and the final fault diagnosis accuracy obtained is only 0.86667. Figure 6b indicates the running results of RFR-GA-BLS. The final fault diagnosis accuracy obtained is 0.98889 for the first 20 features. In addition, the derived time variation is shown in Figure 7. The specific optimization-seeking parameters and corresponding results are shown in Table 4.  
Figure 7: Time-varying state of CFR-GA-BLS and RFR-GA-BLS. Figure 7 shows the time consumed for each feature addition. The CFR-GA-BLS satisfies the end condition only because of the first 55 features screened. Therefore, its running time is included in the next four diagnoses, which are used as a comparison diagnostic rate for skipping out of the loop. Its total run time is 2374.18 s. Similarly, the run time of RFR-GA-BLS is 918.28 s, which is about 15 min. Its speed is much smaller than that of CFR-GA-BLS, which is about 2/5. Table 4 lists the model run time, accuracy, number of features and parameters. Compared with CFR-GA-BLS, RFR-GA-BLS can apply a shorter time to show the best results, and its final selected features are the top 20. After optimization by GA, N1, N2 and N3 are 24, 19 and 544, respectively. Figure 8 illustrates the confusion matrix of RFR-GA-BLS. It clearly shows the distinction between the categories that generate errors. A sample that should belong to the OU10 category is incorrectly distinguished as OU05. To determine whether the features, parameters and accuracy found by RFR-GA-BLS perform well, the model state was manually changed to remove the jump-out condition and run 72 times. The features sorted by CFR and RFR were sequentially imported into GA-BLS, and the change in accuracy was observed for each round, and the results are shown in Figure 9. The running time of each round of GA-BLS was exported, and the results are shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Time** & **Accuracy** &
\begin{tabular}{c} **The Number** \\ **of Features** \\ \end{tabular} & **N1** & **N2** & **N3** \\ \hline CFR-GA-BLS & 2374.18 s & 0.86667 & 55 & 28 & 20 & 557 \\ RFR-GA-BLS & 918.28 s & 0.98889 & 20 & 24 & 19 & 544 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fault diagnosis results. Figure 8: Confusion Matrix for RFR-GA-BLS. According to Figure 9, it can be concluded that the CFR-GA-BLS model satisfies the end condition at the first 55 features, but its accuracy does not reach the optimal value. After the number of features is 65, the accuracy rate jumps. In the case of RFR-GA-BLS, the condition is satisfied at the number of features 20. Although 25 features can reach 1, 20 features make its operation time shorter, and the smaller accuracy gap can be ignored. Therefore, RFR-GA-BLS can achieve more efficient and accurate fault diagnosis. Figure 10 shows the running time of each GA-BLS.