## 4 Discussion  
The last 9 months of data available were used as a test set. The performance of the autoencoder as an anomaly detector was compared to other algorithms that have been widely used for anomaly detection tasks. Isolation forest and one-class support vector machine were tested. Details on these algorithms can be found in References [25; 26]. The same post-processing methodology was applied to all algorithms. Results are presented in Figure 4. Three risk-areas were identified based on the generator's health indicator value distribution. Table 8 provides the information to assess the accuracy of the predictions, and major component replacements that took place during the testing phase are reported. All three algorithms, when merged with alarm information, are able to satisfactorily isolate faulty turbines from the rest. Autoencoder is selected as the algorithm of choice to analyze SCADA data, since it is able to better diagnose faulty turbines even in the absence of alarms data, as in the case of turbine WT13. Moreover, the autoencoder better identifies the high-speed shaft-bearing fault, where isolation forest could not separate it sufficiently and one class Support Vector Machine (SVM) positioned it on the frontier between the warning and safe areas, the ability to identify various failure modes holds large relevance in the selection of the algorithm. Analyzing the results of the autoencoder, it can be noticed that most of the turbines in the critical (red) and dangerous (yellow) areas required replacement of the bearings or brushes of the generator. None of the windmills located in the safe (green) area required maintenance. A detailed study of the data of WT09 and WT12 was done due to their high anomaly count and absence of maintenance intervention. The input variable distributions of all the signals and some other key variables of the generator have been reviewed thoroughly to understand the reason why the autoencoder has found these turbines to be anomalous. The most relevant relationships related with generator failure are presented here and discussed. In Figure 5, the distribution of the probability density of the temperature difference across the two sides of the generator bearing of turbine WT09 are represented, compared with the mean value of the  
Figure 4: Comparison of the results obtained by the three implemented algorithms hybridized with alarms information. The higher the distance from the origin, the worse the conditions of the generator. Three areas are identified according to the health status: healthy (green), warning (yellow) and danger (red). The shape determines the presence and type of fault occurred. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Turbine** & **Maintenance Description** & **Component** \\ \hline WT13 & Bearing High Speed Shaft replacement & Gearbox-Generator \\ WT11 & Generator brushes replaced & Generator \\ WT11 & Generator bearing Non-Drive End replaced & Generator \\ WT10 & Generator bearing Non-Drive Endreplaced & Generator \\ WT08 & Generator bearing Non-Drive Endreplaced & Generator \\ WT07 & Generator brushes replaced & Generator \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Principal maintenance intervention occurred during the testing phase. windfarm and the characteristic curve of this temperature difference with respect to nominal power. It can be observed that the behavior of turbine WT09 is widely different from the rest of the windfarm. These considerations lead us to categorizing this prediction as early fault alert of the generator bearing conditions, rather than a false alarm. Figure 6 shows that turbine WT12 is characterized by an anomalous distribution of the generator stator temperature, in fact the standard deviation of its recorded values is larger than the value of the windfarm, meaning that the generator of this turbine is subjected to less stable operating conditions. This case can also be considered anomalous and worthy of a technical review of the generator.