# A Robust Wind Turbine Component Health Status Indicator
## 4 Discussion  
The main aspects of multivariable component failure estimation models are addressed and improved. A feature selection procedure and a functional and practical _FI_ definition are provided for different gearbox failure modes. The proposed methodology can indicate failure propagation in advance of failure occurrence with a period of longer than two weeks. Although other components (generator and blades) have been tested by this method, the results were not as satisfactory as the gearbox case and will be studied deeper in the future. Regarding the methodology and the model used, it has been shown that it is a flexible model, capable of capturing the dependence of the variables that define the health of the components. This flexibility is demonstrated by the model's successful application to various wind turbine components, such as the gearbox, generator, and blades, each with distinct operational characteristics and failure modes. This GMCM model, applied in the proposed way, makes it possible to obtain a health index of the component that is easy to monitor, reducing the computational cost of the decision system for planning the plant's maintenance. As a drawback of the model, it has been detected that sometimes the  
Figure 14: Sample distributions analysis: SCADA signals versus Failure Index results. Site 2 (gearbox). Dark tones represent failure cases, while light tones represent healthy cases. adjustment of the model is costly, affecting the detection sensitivity of the method in the case that the adjustment is not satisfactory. This requires the generation of several fitting scenarios to select the best one. The cubic spline smoothing method was chosen to balance flexibility and simplicity, ensuring effective smoothing without the risk of overfitting associated with higher-order splines. While other smoothing methods may be more appropriate depending on the nature of the signal, the cubic splines here were well suited to our desired outcome. In the next steps, this methodology will be applied in the detection of failure propagation in other components (yaw system, pitch, etc.) and other renewable energy systems like PV plants. Besides, a higher data frequency could be considered in the models to obtain better results and include different variables if available. Furthermore, this method could have applications in improving the estimation of production, taking into consideration the different working conditions captured by the copula model, and it will be tested in future works. Conceptualization, R.L., J.M. and N.Y.Y. ; methodology, R.L., J.J.M. and N.Y.Y. ; software, R.L. and N.Y.Y. ; validation, R.L., J.J.M. and N.Y.Y. ; formal analysis, R.L., J.J.M. and N.Y.Y. ; investigation, R.L., J.J.M. and N.Y.Y. ; resources, R.L. ; data curation, R.L. and N.Y.Y. ; writing--original draft preparation, R.L. and N.Y.Y. ; writing--review and editing, R.L., J.J.M. and N.Y.Y. ; visualization, R.L., J.J.M. and N.Y.Y. ; supervision, J.J.M. ; project administration, R.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: