## 4 Results  
### SCADA Data Analysis  
Figure 5 shows the \(\Delta P\) of the two considered turbines, T.Ref (reference) and T.Tar (faulty). Considering that the curves are computed as the one week mean value of the \(\Delta P\), it can be seen that the progression of the damage is clearly visible in the faulty wind turbine even with a forewarning in the order of six months, confirming that this parameter  
Figure 4: Flow chart of TCM analysis: features extracted from high resolution vibration data were processed with PCA from which it resulted that PC1 and PC2 could be profitably used as discrimination parameters. In the end, a novelty index (NI) was defined to separate the dataset of healthy turbines from damaged ones. Figure 3: Example of PCA dimension reduction: the principal components PC1 and PC2 are obtained as the mutually orthogonal directions where the variance of the original data is maximized, respectively, labelled as \(\sigma_{1}\) and \(\sigma_{2}\). can be considered a valuable indicator by the point of a preventive condition monitoring strategy. In spite of this, the possibility to uniquely identify the origin of the fault is still lacking as the single lubricating oil circuit serves multiple mechanical components, and so, considering only the \(\Delta P\) of its filter, it is still not possible to find the source of the debris, and, as a consequence, the faulted element. For this purpose, the temperature sensor signals are included in the analysis: as can be seen in Figure 6, the raw data of temperatures from SCADA do not allow a clear identification of the fault, as no visible differences can be found between the reference turbine and the faulted one. If the percentage difference is taken into account, its maximum reaches the value of about 3% on the two months before the fault, which cannot be considered a valuable threshold to detect the damage: such a low difference may even be ascribed to external random variability, as the turbines may have slightly different environmental or operative conditions. Applying the SVR method to the temperature SCADA data, it becomes possible, at first, to amplify the differences between the healthy and the faulty turbines. In Figure 7 the differences between the actual \"Gear Bearing\" temperature and the simulated one are rep  
Figure 5: Evolution of oil filter pressure (in September 2020 the faulted turbine was stopped up to January 2021). Figure 6: Gear bearing temperature trend. sented. Thanks to this method, the variability introduced by environmental or operational conditions becomes less influential, as the value of the error \(T_{actual}-T_{sim}\) is computed separately for each turbine and it can only be ascribed to a different behavior of the real turbine with respect to the simulation obtained by the trained model. A positive drift of the error represents a clear symptom of the heating of the component due to an incoming fault. It is also noticeable to consider, in addition, that in the period May 2020-July 2020 the raw SCADA do not show any anomaly, while the SVR-processed data are able to identify an incipient fault, as the differences between the two curves are still limited but an increasing trend is visible. This kind of behavior was observed only on the temperatures measured near the gear. The most responsive temperature channel was the gear bearing C (see Figure 7), which is the last bearing supporting the gear on the second stage as the shaft input the power to the high speed parallel stage (see Figure 1). Any advice of the incoming fault was discovered on the generator bearing or the main bearing (Figure 8) supporting the slow input shaft. The trend of the error of the SVR model for the gear bearing temperature is able to isolate the faulty drift four months before the turbine stops. In light of the fact that the model was trained using the data of the first three months of 2020, this can be considered a valuable result.