# 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  
All experiments were completed on a Windows 10 computer system equipped with Inter 1.70 GHz and 16 GB of RAM by using pyCharm 2021. To demonstrate the effectiveness and model simplicity of CBNLM-BLS for wind turbine blade damage detection, the algorithms in this paper were compared with existing mainstream methods in terms of classification capability and model structure, ResNet101 [32], VGG19 [33] and AlexNet [34]. The deep learning network hyperparameters involved were tuned based on backpropagation. The initial learning rate was set to \(10^{-5}\) and the activation functions were all RELU. In the CBNLM-BLS, the regularization parameter for ridge regression was set to \(10^{-8}\). The relevant parameters, \(\omega_{e_{i}}\) and \(\beta_{e_{i}}\), were extracted from a standard unified distribution of \([-1,\,1]\) and the tansig function was chosen for the enhancement node layer. A line chart for the variation in damage recognition accuracy with the number of iterations for each neural network algorithm is shown in Figure 6. It could be seen from the figure that the accuracies of ResNet, VGG19 and AlexNet did not increase with the increase in iterations, but remained unchanged within a certain range. Different from other deep networks, the accuracy of the CBNLM-BLS was changed by adjusting the number of nodes. The horizontal coordinate in Figure 6d represents the number of increases of enhancement nodes. The figure shows that the accuracy of the CBNLM-BLS decreased with the increase in the number of nodes. This was because the enhancement nodes that were converted were sufficient to extract enough features to obtain an accurate classification result after the images were fed into the model. In addition to the comparison in terms of accuracy, the four metrics of precision and recall as well as F1 score and time were introduced. For the problem of two kinds of classification for damage detection in wind turbine blades, the image samples were divided into cases of true positive (TP), false positive (FP), true negative (TN) and false negative (FN) according to the combination of actual categories and predicted categories. A confusion matrix of classification results is shown in Table 2. The results of the comparison of the different models are shown in Table 3.  
Figure 5: Flowchart of damage detection in wind turbine blades based on the CBNLM-BLS. The accuracy indicated the number of correct samples predicted as a proportion of the total sample. \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}, \tag{10}\]  
The precision represented the probability that all samples with a positive prediction were actually positive. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{11}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Actual Categories**} & \multicolumn{3}{c}{**Predicted Categories**} \\ \cline{2-4}  & **Positive** & **Negative** \\ \hline Positive & TP & FN \\ \hline Negative & FP & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Explanatory table for evaluation indicators. Figure 6: Line chart of model accuracy variation with number of iterations. (**a**) Line chart of ResNet accuracy variation with number of iterations; (**b**) line chart of VGG19 accuracy variation with number of iterations; (**c**) line chart of AlexNet accuracy variation with number of iterations; (**d**) line chart of CBNLM-BLS accuracy variation with number of nodes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1\_Score (\%)** & **Times (s)** \\ \hline ResNet & 81.035 & 94.444 & 37.778 & 53.968 & 35,939 \\ AlexNet & 95.700 & 81.818 & 100 & 90.000 & 11,898 \\ VGG19 & 82.609 & 82.609 & 100 & 90.476 & 13,063 \\ BLS & 99.107 & 100 & 98.276 & 99.131 & 51.126 \\ CBNLM-BLS & 99.716 & 98.630 & 100 & 99.310 & 28.662 \\ \hline \hline \end{tabular}
\end{table}