## 3 Results
### Overall Results  
As the results of the individual predictors are not sufficiently good, the authors present a hybrid technique that merges the two systems in a more complete predictor, as detailed in Section 2.3. Table 6 presents a summary of the results. The turbines that were obtaining higher values for the health KPIs were reported. Examining the reported turbines and the maintenance log, the results table was done. During the test period, problems such as broken generators, consumed generator brushes or generators bearing damages were encountered. It can be seen that most of the reported turbines were found to have some problems; moreover, the results across the various windfarms are consistent. The accuracy never gets lower than 70% and the overall Kappa is 56%. The advantages of using a hybrid predictor are clear when its results are compared to the ones of the autoencoder and alarm predictors. The number of TPs increased substantially, and remarkably, the number of FPs was halved. The two components of the composed predictors are complementary, allowing for more accurate and reliable predictions. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **Brand** & **Location** & **TP** & **FN** & **FP** & **TN** & **Accuracy** & **Kappa** & **Sensitivity** & **Specificity** & **Precision** & **F1** \\ \hline
**WF1** & Vestas & Spain & 3 & 1 & 4 & 3 & 55\% & 15\% & 75\% & 43\% & 43\% & 55\% \\
**WF2** & Vestas & Spain & 1 & 3 & 2 & 3 & 44\% & \(-\)15\% & 25\% & 60\% & 33\% & 29\% \\
**WF3** & Siemens & Poland & 1 & 0 & 6 & 11 & 67\% & 17\% & 100\% & 65\% & 14\% & 25\% \\
**WF4** & Siemens & Poland & 3 & 3 & 1 & 8 & 73\% & 41\% & 50\% & 89\% & 75\% & 60\% \\
**WF5** & Senviron & Poland & 4 & 1 & 4 & 4 & 62\% & 27\% & 80\% & 50\% & 50\% & 62\% \\
**WF6** & Senviron & Poland & 3 & 1 & 6 & 12 & 68\% & 28\% & 75\% & 67\% & 33\% & 46\% \\
**WF7** & Nordex & UK & 2 & 4 & 7 & 13 & 58\% & \(-\)1\% & 33\% & 65\% & 22\% & 27\% \\ \hline
**TOTAL** & & & 17 & 13 & 30 & 54 & 62\% & 18\% & 57\% & 64\% & 36\% & 44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results obtained using the autoencoder. WF stands for Windfarm, TP True Positive, FN False Negative, FP False Positive and TN True Negative. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **Brand** & **Location** & **TP** & **FN** & **FP** & **TN** & **Accuracy** & **Kappa** & **Sensitivity** & **Specificity** & **Precision** & **F1** \\ \hline
**WF1** & Vestas & Spain & 2 & 2 & 1 & 6 & 73\% & 38\% & 50\% & 86\% & 67\% & 57\% \\
**WF2** & Vestas & Spain & 2 & 2 & 1 & 4 & 67\% & 31\% & 50\% & 80\% & 67\% & 57\% \\
**WF3** & Siemens & Poland & 1 & 0 & 17 & 0 & 6\% & 0\% & 100\% & 0\% & 6\% & 11\% \\
**WF4** & Siemens & Poland & 2 & 4 & 2 & 7 & 60\% & 12\% & 33\% & 78\% & 50\% & 40\% \\
**WF5** & Senviron & Poland & 4 & 1 & 0 & 8 & 92\% & 83\% & 80\% & 100\% & 100\% & 89\% \\
**WF6** & Senviron & Poland & 4 & 0 & 6 & 12 & 73\% & 42\% & 100\% & 67\% & 40\% & 57\% \\
**WF7** & Nordex & UK & 3 & 3 & 4 & 16 & 73\% & 28\% & 50\% & 80\% & 43\% & 46\% \\ \hline
**TOTAL** & & & 18 & 12 & 31 & 53 & 62\% & 19\% & 60\% & 63\% & 37\% & 46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results obtained using an alarm-based predictor. WF stands for Windfarm, TP True Positive, FN False Negative, FP False Positive and TN True Negative. The Receiving Operator Curve (ROC) is calculated to represent the predictive power of the proposed methodology and its response to adjustments in the cutoff value to apply to the health status vector. In Figure 2, the ROC curves of the different windfarms are presented. The cutoff values are adjusted for each wind farm to obtain optimal results. Figure 3 represents the dataset as a whole, without distinction between the different windfarms and simulating the effect of a unique cutoff value. The two dashed line defines the values of the false positive rate and true positive rate that can be obtained by selecting the optimal cutoff value for each windfarm.