## 5 Results and Discussion
It can be observed that adding a few features does not increase the GA-BLS operation time, and the running time is kept within 30-50 s. Therefore, width learning has great advantages as an optimization model. In addition, this article verifies the superiority of GA-BLS. In order to reduce time consumption, only the first 20 features obtained in the previous experiments are selected for fault diagnosis. The comparison method was set up for comparison, as shown in Table 5.  
The BLS model was chosen with the parameters of the code shared by the original authors, i.e., N1 = 10, N2 = 10, N3 = 500. The optimal accuracy obtained was 0.93333 with a  
Figure 10: Running time of each GA-BLS (**a**) CFR-GA-BLS; (**b**) RFR-GA-BLS. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Times**} & **Consumption** & \multirow{2}{*}{**Accuracy**} & **Parameter (N1,** \\  & & **Time Indicator** (**BLS is 1**) & & **N2, N3**) \\ \hline GA-BLS & 40.81 s & 226 & 0.98889 & 24, 19, 544 \\ BLS & 0.18 s & 1 & 0.93333 & 10, 10, 500 \\ PSO-BLS & 39.20 s & 1 & 0.97778 & 19, 22, 550 \\ Grdy-BLS & 5.83 s & 32 & 0.95556 & 11, 10, 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the operating results of the method. Figure 9: Fault diagnosis accuracy of all features (**a**) CFR-GA-BLS; (**b**) RFR-GA-BLS. running time of 0.18 s. Particle Swarm Optimization (PSO) was applied to determine the three parameters of the BLS model, and the number of iterations was again chosen to be 10. The accuracy is 0.97778, and the running time is 39.20 s. The Grdy-BLS using greedy algorithm (Grdy) to optimize the BLS obtains an accuracy of 0.95556 and the running time is 5.83 s.  
Although the BLS model has a short running time, the accuracy of the model is low due to the poor generalization ability of the model by using only the parameters provided in the original text. PSO-BLS consumes slightly less time than GA-BLS, but the time difference is not significant. Its accuracy is lower than that of GA-BLS. Grdy-BLS has a short computing time, but the greedy algorithm tends to fall into a local optimum. As seen, the three parameters chosen fluctuate only in the initial state. Therefore, GA-BLS is a good choice. As part of RFR-GA-BLS, it shortens the operation time and does not easily fall into the local optimum. Not only the good parameters and the minimum number of features are found, but also the optimal accuracy is obtained. Subsequently, the first 20 features were used for fault diagnosis using SVM and RF. As shown in Table 6, SVM and RF are inferior to BLS in terms of speed and accuracy. The running time of SVM is 3.32 s, and that of RF is 2.19 s, which is faster but difficult to compare with BLS, and the algorithm will be exponentially higher if optimization parameters such as GA are added. In addition, 1 DCNN and 2 DCNN were applied for fault diagnosis of infrared images. The top 20 features of the ranking were processed using 1 DCNN, using two layers of convolution and two layers of pooling. The epoch was chosen to be 100. The speed was similar to that of GA-BLS, but only an accuracy of 0.9333 was obtained. The 2 DCNN was used to process the image after adaptive thresholding, using two layers of convolution and two layers of pooling. The epoch was chosen to be 30. Still following the 9:1 dataset partitioning. Although it achieves an accuracy of 0.9889, it takes 10,147 times longer than BLS. The speed is also much slower than RFR-GA-BLS. Thus, GA-BLS not only achieves the parameter search, but also has more advantages in terms of speed and accuracy. The problem of low accuracy of traditional machine learning fault diagnosis is overcome, and the problem of time-consuming deep learning of direct image processing is also overcome. The RFR-GA-BLS proposed in this article has strong advantages in the field of infrared image fault diagnosis of bearings.