# A Robust Wind Turbine Component Health Status Indicator
## 3 Results
### Results Site 1  
In site 1, the available variables were limited, but the registered component failure in the maintenance logbook was available on different components (generator, blades and gearbox). Consequently, for this site, three failure cases are presented, one per component (S1_GEN_F1, S1_BLD_F1, S1_GBX_F1) and nine healthy cases (S1_GEN_H1, S1_GEN_H2, S1_GEN_H3,S1_BLD_H1, S1_BLD_H2, S1_BLD_H3, S1_GBX_H1, S1_GBX_H2, S1_GBX_H3), three for each component (generator, blades, and gearbox). As the first result of the behavior of the methodology applied through the values obtained from the _FI_ index at this site, the graphs in Figure 7 are shown, where each row corresponds to the three healthy cases of each component. This figure displays the range of _FI_ values in all the healthy cases analyzed at the site. From the analysis of this figure, the first conclusion regarding the performance of the _FI_ index is drawn. In general, the healthy state of the component corresponds to _FI_ values equal to or less than 50. Subsequently, each of the failure cases studied for this site is analyzed. This analysis includes the _FI_ index along with all the variables considered and associated with the component's health. Additionally, for comparative purposes, a healthy component case is included along with each failure case. **Site 1, Faulty case 1 (generator)** presents the analysis performed in the faulty working period of S1_GEN_F1 (investigation of generator case). Figure 8a shows the _FI_ trend and the SCADA variables under consideration. The _FI_ ranges between 65 and 70. In this case, major maintenance intervention on the generator was reported in July 2015. Before replacing the component, between 15 May and 15 June, an increase in the _FI_ value between 35 and almost 70 was observed. Although similar increases were detected in the weeks prior to the test period, the model seems to detect anomalies before the intervention. Despite there being room for improvement in terms of sensitivity, the method appears to be effective in detection. **Site 1, Healthy case 1 (generator)** presents the analysis performed in the healthy working period of S1_GEN_H1 (investigation of generator case). Figure 8b shows the _FI_ trend and the SCADA variables under consideration. The _FI_ ranges between 30% and 60%. In accordance with the FI, the SCADA signals do not show signs of abnormal working. The SCADA signals do not exhibit significant gaps during the analysis period. There are no significant mean changes in the time series of these signals. Only from March, there is a slight decrease in the temperature of the large generator and the small generator, but it corresponds with a slight decrease in wind speed in the same period. Figure 7: Failure Index Evolution (_FI_). Components in a healthy working mode. Site1. While the differences between the healthy case and the faulty case are not as evident as in other components analyzed in this study, they do indicate a reasonable sensitivity to anomaly detection in generators. Therefore, although it would be beneficial to continue investigating by applying complementary techniques and analyzing the data at a lower granularity, the proposed method does appear to provide useful information for the detection of anomalies in generators. **Site 1, Faulty case 2 (blades)** presents the analysis performed in the faulty working period of S1_BLD_F1 (investigation of blades case). Figure 9a shows the \(FI\) trend and the SCADA variables under consideration. The \(FI\) ranges between 40% and 66%. In this case, major maintenance interventions on blades are reported on 4 December 2014. In this case, in order to detect some degree of degradation in blades and to monitor the evolution of the \(FI\) index before and after major corrective actions, the component is monitored between January 2013 and July 2016. A significant increase in the \(FI\) value is observed from December 2014, which corresponds to the date of the major corrective.