# Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model
## 3 Damage Detection in Wind Turbine Blades Based on the CBNLM-BLS
### Results and Discussion

Table 3: Comparison of the classification results in different classification algorithms for wind turbine blade damage. Recall represented the probability that of samples that were actually positive were predicted to be positive. \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}^{\prime}}, \tag{12}\]  
F1_Score was a weighted harmonic mean of precision and recall. \[\text{F1\_score}=\frac{2\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}, \tag{13}\]  
As can be seen from Table 3, in the experiments for damage detection in wind turbine blades, the CBNLM-BLS model proposed in this work had the highest classification accuracy of 99.716%. While the BLS, ResNet101, AlexNet and VGG19 algorithms had accuracies of 99.107%, 81.0%, 95.7% and 82.6%, respectively. Compared with other models, the BLS model had the highest precision of 100%. The precision of the CBNLM-BLS was 98.63%, which was slightly lower than that of the BLS. Although the recall rate of the CBNLM-BLS was the same as AlexNet and VGG19, the accuracy of CBNLM-BLS was better than other models. The F1-score value of CBNLM-BLS was bigger than the other four, indicating that the classification effect of the model was the best. The two algorithms of the BLS and the CBNLM-BLS were both much faster than the other three deep neural networks, indicating that they could overcome the weakness of the large time consumption and the high complexity of structure in deep learning neural networks such as ResNet, AlexNet and VGG-19. Compared with the traditional BLS, the training time of the CBNLM-BLS was shorter, at 28.662 s. It revealed that by using chunked parallelism, the computing speed of the CBNLM-BLS algorithm was greatly improved and it had less time complexity than the BLS. Figure 7 shows the structures of ResNet, CBNLM-BLS, AlexNet and VGG19, respectively. The structural diagrams show that the CBNLM-BLS had no layer-to-layer coupling, no multi-layer connections, no more parameters to save and no need to update the weights using gradient descent. Parametric analysis of the proposed CBNLM-BLS for damage detection in wind turbine blades is shown in Table 4, when the network parameters took different values, where \(N_{1}\) denoted the number of windows, \(N_{2}\) denoted the number of nodes per window and \(N_{3}\) denoted the number of enhancement nodes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{N_{1}}\) & \(\mathbf{N_{2}}\) & \(\mathbf{N_{3}}\) & **Accuracy (\%)** & **Times (s)** \\ \hline \multirow{3}{*}{10} & \multirow{3}{*}{10} & 100 & 99.639 & 28.301 \\  & & 150 & 99.639 & 27.211 \\  & & 200 & 99.819 & 27.327 \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{10} & \multirow{3}{*}{50} & 99.097 & 14.543 \\  & & & 99.639 & 27.891 \\ \cline{1-1}  & & & 99.639 & 41.060 \\ \hline \multirow{3}{*}{10} & \multirow{3}{*}{10} & 5 & 99.278 & 14.078 \\  & & 10 & 99.639 & 27.327 \\ \cline{1-1}  & & 15 & 99.639 & 41.752 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the results of the CBNLM-BLS for damage images of the wind turbine blade on the collected image dataset when taking different parameters. As can be seen in Table 4, the accuracy of the CBNLM-BLS did not increase with the increase in \(N_{3}\), and the training time changed in a certain range when \(N_{1}\) and \(N_{2}\) were set to be unchanged. The accuracy did not increase with the increase in \(N_{1}\), and the training time increased accordingly when \(N_{2}\) and \(N_{3}\) were set to be unchanged. When \(N_{1}\) and \(N_{3}\) remain unchanged, the training time increased with the increase in \(N_{2}\), but the accuracy did not increase with the increase in \(N_{2}\), indicating that the classification accuracy of the random broad learning network, in a certain range, and the increase in \(N_{1}\), \(N_{2}\) and \(N_{3}\) would make the extracted features richer and would obtain the best number of features in that range; thus, getting a higher classification. At the same time, as the nodes increased, the time also increased.