# A Robust Wind Turbine Component Health Status Indicator
## 2 Materials and Methods
### Data  
Data were obtained from two different sites in Spain. In Site 1, there are four wind farms with 33 wind turbines each and a nominal power of 950 kW per wind turbine. In Site 2, there is one wind farm comprising 12 wind turbines with a nominal power of 3000 kW each; 10 min SCADA data, together with failure and maintenance logbooks, were used in the study. SCADA signals had to be cleaned to make them usable. Then, each failure was assigned to the respective wind turbine (WT) component with the help of the SCADA alarms and logbooks. The method has been previously tested in site 1, where the information related to reported failures was available in different components (generator, blades and gearbox). Later, in site 2 the study focuses on gearbox failures due to the high number of reported failures of this component in the available period of this site. Inspections and regular service work periods were excluded from the database. Available data were divided into two datasets corresponding to the learning and test periods. Then, three failure cases and nine healthy cases (generator, blades and gearbox) were modeled in site 1, and eight cases were modeled in site 2, two of them containing a gearbox failure and six without failure. The optimal splitting of the data into the training and the test periods for supervised learning techniques ranges from 60% to 80% for the training period (being the remaining data for the test period) [39; 40]. Therefore, approximately six months of data were used for learning the model and, approximately two months of data were used for testing it. Maintenance interventions and failure history have been documented as unstructured comments in spreadsheets. A simplified summary of the maintenance history excluding routine services is provided in Table 2.  
On site 1, S1_GEN_F1, S1_BLD_F1 and S1_GBX_F1 are analyzed as generator failure, blade failure and gearbox failure cases. In addition, S1_GEN_H1, S1_GEN_H2, S1_GEN_H3, S1_BLD_H1, S1_BLD_H2, S1_BLD_H3, S1_GBX_H1, S1_GBX_H2 y S1_GBX_H3 are also studied as healthy cases for comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Wind Turbine** & **Start Date** & **End Date** & **Event** \\ \hline
1 & S1\_GEN\_F1 & 1 July 2015 & – & Generator failure \\ \hline
1 & S1\_BLD\_F1 & 4 December 2014 & – & Blades failure \\ \hline
1 & S1\_GBX\_F1 & 22 May 2012 & – & Gearbox failure \\ \hline
2 & S2\_GBX\_F1 & 7 May 2014 & 31 July 2015 & Gearbox failure \\ \hline
2 & S2\_GBX\_F2 & 5 June 2014 & 1 August 2014 &
\begin{tabular}{c} Gearbox oil pump \\ pressure failure \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Maintenance logbook events. On site 2, S2_GBX_F1 and S2_GBX_F2 are analyzed as gearbox failure cases. Similarly to site 1, six cases without failure are also studied as S2_GBX_H1, S2_GBX_H2, S2_GBX_H3, S2_GBX_H4, S2_GBX_H5, S2_GBX_H6. Between the two sites, five failure cases and 15 healthy cases have been analyzed. This results in a ratio of three healthy cases for each failure case analyzed. In the case of site 1, the number of available SCADA signals is limited to 16 variables and all will be considered in the study. Whereas, in the case of site 2, SCADA data provide a total of 97 signals. Taking into consideration the component to be studied and literature-based recommendations [41], 13 signals were selected to model gearbox healthy and faulty working behaviors. Table 3 and Table 4 show the list of variables of site 1 and site 2, respectively. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Signal** & **Abbreviation** & **Unit** \\ \hline \hline \multicolumn{1}{c}{Gearbox bearing temperature 1} & BT1 & \({}^{\text{\text{\  
In the end, a representative data sample has been considered. Later, data cleaning was applied to the signals and registration errors and duplicated data were removed. In general, the data availability values are very high after this process, exceeding 95% in most cases (except for S1_GBX_H1, which is around 93%), making it a representative data sample.