# A Robust Wind Turbine Component Health Status Indicator
## 1 Introduction
In brief, O&M engineers need to simultaneously monitor a wide range of variables to assess the health status level of different wind turbine components. Therefore, easy-to-track summary indicators are required. These indicators can be considered as a decision criterion for intervention planning, component repair and asset remaining lifetime evaluations [10]. Furthermore, a good prognosis of the wind power plant and adapted management of the maintenance to the specific working conditions will revert very positively to the economic balance of each project. The new methodologies, which aim for \"Condition Monitoring\" and predictive maintenance of the assets, are becoming more promising [27; 28; 29]. Copula models are proposed in [30; 31] for power curve performance analysis with applications in condition monitoring. In [32] the Gaussian Mixture Copula Model (GMCM) is proposed as a new modeling tool for power curves. The results are promising in modeling and outlier detection in power curves. It is expected that additional variables will be included to obtain a more accurate model in the future. One of the first advanced approaches in wind turbines from a multivariate environment [33], obtains a power curve multivariate model based on conditional copulas. Reference [34] explores the multivariate analysis considering generator speed, wind speed and power in multivariate models for wind turbine health monitoring. This kind of tool allows estimating the joint probability density function of considered variables in a non-parametric way. These models can satisfy the need for a methodology to characterize the relationship between critical variables in a multivariate environment. They can be applied to power curves, and components characterization and they are suitable to be applied on preventive detection of failures. As a summary and to support the final motivation and justification for the selection of the proposed methodology, Figure 1 and Table 1 are shown. Figure 1 illustrates the typical data used in condition monitoring, as well as the most commonly employed models. It highlights the scope of this paper, which focuses on SCADA data and probabilistic models. Table 1 presents a representative selection of the prevailing trends and dominant models in fault detection and diagnostics within wind turbine components, based on SCADA data, with applications in condition-based maintenance. SCADA data may be a preferable choice for condition monitoring, over well-tested techniques like vibration-based, due to its availability and low cost of exploitation, providing a comprehensive view of equipment performance. Leveraging existing infrastructure, SCADA enables effective anomaly detection and predictive maintenance, enhancing operational efficiency and minimizing downtime. As extracted from the consulted bibliography, Table 1, and other comprehensive and recent reviews in the field ([18; 27; 38]), most references on wind turbine fault detection methods are based on condition monitoring techniques, such as signal-based or model-based approaches. Furthermore, the main models used in CM in recent years appear to have focused on data-driven classification and regression models. There is also a noticeable interest in other probabilistic and deep learning models. The proposed method can be considered as a combination of both approaches, leveraging the strengths of signal-based and model-based methods. It utilizes a model-based approach by employing the GMCM model to define the wind turbine component's behavior. This model is trained using selected variables identified through a feature selection process and captures statistical relationships and dependencies among them. Additionally, it incorporates a signal-based approach by using signals obtained from the wind turbine, which can be historical or real-time data. These signals capture dynamic and changing