## 3 Results and Discussion
### Debonding Damage Identification  
The natural frequencies of damaged blades (Dataset No. 2-Dataset No. 1105 in Table 3) were normalized by the natural frequencies of undamaged blades (Dataset No. 1 in Table 3) to have uniformity in the data defined in Section 3.1. The natural frequency change rate and the average for each natural frequency mode were accordingly determined, as shown in Table 4. The natural frequency of the 3 rd, 5 th, and the 6 th, at which the change rate appears to be larger, were selected as relevant key features because they showed higher sensitivity on the damage configurations. Then, ANN-based machine learning analysis was performed to identify the correlations between these features and the damage variables and to define the identification model. Here, the natural frequencies of the 3 rd, 5 th, and the 6 th for each of the damage configurations were applied as the input-layer data, and the debonding damage configurations were chosen as the output-layer data. In addition to this, 70% of the debonding damage configurations were used as the training data and 30% as the verification data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{6}{c}{**Frequency Change Rate [\%]**} \\  & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** & **6 th** \\ \hline
1 & & \multicolumn{6}{c}{Reference data for undamaged blade} \\
2 & 0.01 & 0.01 & 0.01 & 0.00 & 0.01 & 0.05 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
41 & 0.12 & 0.06 & 0.77 & 0.28 & 1.75 & 0.44 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
50 & 0.01 & 0.01 & 0.02 & 0.01 & 0.03 & 0.17 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
164 & 0.06 & 0.01 & 0.04 & 0.03 & 0.21 & 0.33 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
242 & 0.02 & 0.02 & 0.06 & 0.04 & 0.18 & 0.13 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
1105 & 0.41 & 0.02 & 0.44 & 0.31 & 2.93 & 2.80 \\ \hline Average & 0.16 & 0.03 & 0.40 & 0.23 & 1.03 & 1.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Frequency change rates and averages. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multicolumn{6}{c}{**Frequency [Hz]**} \\  & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** & **6 th** \\ \hline
1 & 0.95 & 1.24 & 2.78 & 4.62 & 5.92 & 6.92 \\
2 & 0.95 & 1.24 & 2.78 & 4.62 & 5.92 & 6.92 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) &  
The analysis results showed an overall damage identification accuracy of 84%, as shown in Figure 8. Here, the \(x\)-axis represents the predefined debonding damage variables for 1105 sets of debonding damage configurations defined for training and the \(y\)-axis represents the identified debonding damage variables obtained through the proposed ANN-based damage identification model. Here, the dotted line indicates the reference line of \(R=1.0\) and the solid line shows the regression line for the identification results by the ANN-based damage identification model. These results show that the proposed ANN-based damage identification model has a high accuracy of about 84%. For further understanding of the results from the proposed model, a detailed analysis was conducted for each damage configuration type. Figure 9 indicates that the identification accuracy of Type 1 damage, with single damage at one joint, was quite high, at 97%. However, the reference (dotted line) and identified results obtained through the training data (solid line) do not match in all ranges because only 34 of the 773 training data contained Type 1 damage. Thus, it is difficult to assume that all the identified results are reliable because such a small number of damage configurations were used. Figure 9 indicates that the identification accuracy of Type 2 damage, with two damages at one joint, was even higher than for Type 1 at about 98%.