# A Robust Wind Turbine Component Health Status Indicator
## 1 Introduction  
Wind energy is one of the fastest-growing energy sources. Both onshore and offshore wind technologies pose Operation and Maintenance (O&M) challenges for O&M decision-makers such as limited site accessibility [1], adverse and dynamic operating conditions [2] and the aging of the wind turbine fleet [3]. Lack of proper management under these challenging circumstances results in severe penalties such as major component failures, long unavailability and significant revenue losses. To avoid severe consequences, there exists a growing interest in asset digitalization and timely status tracking of wind power plants [4, 5, 6]. This interest results in new data acquisition opportunities, new data sources and the tendency to move from data silos to data sharing. The available data must be analyzed and interpreted smartly, which is highly beneficial to decision-makers. Then, smart management of O&M activities is relevant to enhancing the economic balance of a wind energy power plant. Furthermore, the success of maintenance task optimization models relies on accurate estimation of the component's remaining useful life (RUL), the impact of component degradation on system efficiency, and the accuracy of the prognosis model itself ([7, 8]). Therefore, the use of health indices that can be easily monitored over time can enhance the precision of these models and facilitate the field maintenance personnel's tasks in the power plant. Smart management of O&M activities involves planning predictive and preventative interventions, evaluation of assets performance and effective condition monitoring. Compact decision support tools are needed to transform such complex decisions into a manageable form and make consistent decisions [9]. The decision-maker responsible for managing wind farm O&M activities (wind power plant owner or O&M engineer) needs to gain in-depth knowledge of the machine and its components' health status. It is essential to perform adequate maintenance planning that maximizes asset reliability. The decision support tools must help with these needs and also provide helpful and easy-to-track indicators of anomalous working states promptly [10; 11]. Different malfunction and anomaly detection techniques can be implemented in a wind turbine O&M decision support tool. The majority of the condition monitoring techniques use Supervisory Control and Data Acquisition (SCADA) signals or Condition Monitoring System (CMS) measurements as inputs. A wind turbine is a complex system with many components & sub-components. In wind turbines, the condition monitoring systems target major components such as electric and control systems, generators, hubs, blades and gearboxes, since these components are classified as critical assemblies [12]. The frequency of major wind turbine gearbox failures is generally low. However, these failures are associated with severe consequences [13]. Timely evaluation of CMS and SCADA data trends can aid decision-making on whether to inspect, repair, or replace the wind turbine component under examination [14]. There are also tools for analyzing the remaining useful lifetime of the wind turbine component. These tools use the maintenance and failure records of the component. Nevertheless, many challenges exist in conveying data sets and deriving results from such analyses. On the one hand, existing methods that use only CMS and SCADA data are limited to aid only short-term maintenance decisions. On the other hand, methods based on statistical distributions, which use only maintenance and failure records as input data, cannot provide fine temporal failure occurrence estimations. Moreover, these methods require waiting until a reasonable amount of failures have occurred before obtaining robust findings due to the input failure data requirement of the models [15]. Therefore, new methods are needed to integrate all forms of maintenance, failures and operational data to support the scheduling of component intervention associated with decision making [16; 17].