# A Robust Wind Turbine Component Health Status Indicator
## 2 Materials and Methods
### Flowchart of the FI Generation Process  
The conceptual flowchart of the proposed methodology is given in Figure 2.  
In this analysis, 20 cases are investigated to develop a functional failure index and test its performance. With the 12 cases of site 1 and the available variables, the model's performance has been analyzed for different components (generator, blades and gearbox). The best results have been observed in the case of the gearbox. To confirm the resulting performance in this component, a specific analysis of the gearboxes has been carried out at site 2 where eight cases were analyzed using the same methodology shown in the flowchart given in Figure 2. The methodology is the same in site 1 and in site 2. However, in site 1 it is not as critical as in site 2 because of the limited number of available variables that can affect each component.  
First, signals are reviewed considering literature recommendations for wind turbine component failure modes [41]. Then, all available signals have been considered at site 1. They have been grouped by component by considering 10 variables related to the blade component, nine variables related to the generator and seven related to the gearbox. On the other hand, 13 signals related to the gearbox are selected on site 2.  
In order to ensure that the input data in the learning period represents the healthy period of the wind turbine, the following procedure was performed. Duplicate data and zero power values are removed. Later, power values outside the normal operating range are filtered. For this purpose, data associated with lower or higher production than expected are removed using the Mahalanobis distance [42; 43] between the measured power series and the estimated power for the measured wind speed considering the manufacturer's power curve.  
Next, filtered input data of the SCADA signals are split as learning and test inputs for all wind turbines. Learning data are used in the GMCM and the resulting output from the GMCM (Log-likelihood density) is transformed as the inverse of the cumulative Log-Likelihood density [44] to generate the self-defined failure index (_FI_).  
Figure 2: Diagram. Health Status Indicator Process.  
Then, the feature selection process is conducted. Indeed, this feature selection is applied by component and by site. This process aims at reducing the number of variables to feed the final model. Using Spearman correlation, the generated \(FI\) and the regressor variables are analyzed in both sites to evaluate their influence on the generated \(FI\) series. Then, a backward and forward process is started where a pre-selection criterion is used to assess the overall importance of the variables. As a result, considering the Spearman correlation index and the attempt to homogenize the final models, a GMCM model based on seven variables is established in the learning period and applied to all the studied cases. The seven variables selected as the most influencing for each component model and for each site are listed in Section 2.4. Later, the GMCM coefficients are saved and then used with the test data to evaluate the FI's performance for both sites. Finally, generated failure index series are smoothed using cubic spline methodology [45] to provide a clean summary signal to the decision-maker.