# A Robust Wind Turbine Component Health Status Indicator
## 2 Materials and Methods
### Feature Selection  
Different variables were selected to monitor the generator, blades, and gearbox based on the domain expertise, data availability, and variables' physical meanings. In the case of site 1, 16 signals were available to monitor the components and 97 signals in the case of site 2. In the first stage, the behavior of the GMCM model was tested in three wind turbine components (generator, blades and gearbox) in site 1, where the availability of variables was limited. Once a potential failure detection capacity was observed in the gearbox components of the turbines from site 1, the same procedure was applied in site 2 focused on gearbox failure detection. In order to make a selection of the most interesting set of variables to assess the health of the component, the available variables, their relationship and the experience from the literature review were taken into consideration [41]. In this sense, in site 1, 10 variables have been considered to analyze the blade's health status, nine variables in the case of the generator, and seven variables were available to track the gearbox's health status. In site 2, the number of available signals for the gearbox analysis is higher. Therefore, some additional issues about the variables' physical meanings have been taken into account. In this respect, it was considered that the oil affects the lubrication film directly, and thanks to this, the protection of moving parts can be achieved by reducing the friction in the gearbox. Therefore, oil has to be monitored too, and oil conditions can be monitored by temperature, dielectric constant or viscosity. As highlighted in literature [46], there are already extensively developed analyses and procedures considering oil status for gearbox health condition monitoring. Therefore, the working conditions of the gearbox and oil status have to be taken into consideration in the gearbox health status analysis. Focusing on viscosity is a very relevant characteristic to be considered for the proper functioning of the oil. It is known that oil temperature and oil pressure directly affect the viscosity [47]. Therefore, oil pressure and temperature could be good candidates for consideration as sensitive variables when analyzing the health of a gearbox. Hence, according to these considerations and based on the literature review and available signals, 13 variables have been selected in the first stage as significant enough to be considered in a gearbox health status tracking in site 2. Afterward, to select the most influential variables and reduce their number when generating the model, the Spearman correlation analysis was also carried out [48]. Spearman correlation provides a test to evaluate the degree of association between variables, wherein  
Figure 3: Accepted (blue) and Rejected (orange) learning data. this Spearman correlation coefficient will acquire a higher value as the degree of association increases. Then, the \(FI\) and the regressor variables were analyzed to evaluate their influence on the generated \(FI\) series estimated from different wind turbines in the wind farm. Hereafter, the findings obtained from the Spearman correlation of \(FI\) and the dependent variables are shown in Figures 4 and 5. These figures show the most influential variables in the failure index results allowing us to discard some of them to simplify the models. In order to harmonize the number of variables and generate the models considering the most influential of them, they were reduced to 7 to establish the final GMCM model in the learning period. For this purpose, the variables with a Spearman correlation index greater than 0.18 (V, Pow, M, Vmax, Vvar, Gw, AT) were selected in the case of the blade model (see Figure 4a). In the case of the generator model, the selected variables were associated with Spearman index values greater than 0.22 (V, Ilx, Q, Gw, VIx, GIT, GsT) (see Figure 4b).