# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 1 Introduction  
Global demand for energy has achieved an unprecedented level with the growing world population and the rising industrialization in developing countries. However, globally, the largest amount of energy is obtained from fossil fuels, which is closely related to the rising levels of greenhouse gases emissions. Definitely, energy transition to renewable sources is the crux of the matter to fight climate change. As stated in [1] only 10% of the world's primary energy supply is already from renewable energies, but they are steadily growing. Among renewable energy sources, wind power has been the fastest growing in the recent decades, and in 2019 it was the leading source of new capacity in Europe, the US, and Canada, and the second largest in China [2]. It is noteworthy that wind energy levelized cost of energy (LCOE) fell 39% between 2010 and 2019 [3]. However, from this LCOE, the operation and maintenance (O&M) accounts for 28.5% in land-based wind projects, and up to 34% in offshore wind projects [4]. Energy production losses due to downtime (caused by O&M of the assets), together with the costs associated to the replacement of components can scale up to millions of Euros per year in any industrial size wind park. Thus, it is of paramount importance that the wind industry moves from corrective (repairing components after they break down) and preventive maintenance (scheduled at regular intervals) to predictive maintenance (scheduled as needed based on the asset condition). Predictive, or condition-based maintenance (CBM), provides operators with an advanced warning before the actual fault occurs, allowing them to plan ahead and schedule repairs to coincide with weather or production windows to reduce costs and turbine downtime. CBM is an extensive area of research in a wide varietyof applications, such as smart manufacturing [5]. However, its application to complex systems, such as wind turbines (WTs) that work under different and varying operating and environmental conditions remains an open challenge. Furthermore, the latest CBM developments tend to use expensive specifically tailored sensors for condition monitoring (CM), which is not economically viable for turbines already under operation and even less in case they are close to reach the end of their lifespan. The life expectancy of wind parks from the late 1990s and early 2000s surge of wind power is about to be completed. A decision on lifetime extension is complicated, but it is clear that end-of-life solutions will develop a significant market over the next five years [6]. In particular, CBM data-driven methodologies based on already available supervisory control and data acquisition (SCADA) data are a promising cost-effective solution. These SCADA data are highly variable because of the changing operational conditions, they have a low sampling time (10 min average value), are not standardized, and have not been initially designed for the particular purpose of CM but for control purposes. Therefore, it is a hard challenge to contribute CBM strategies based solely on these data [7]. However, this is an active research area, as shown by recent publications. On the one hand, much of the references found in the literature work with simulated SCADA (e.g., [8]) that can be obtained using open source simulation software and, rarely, real data as these are proprietary data from the wind park operators and are not easily available. On the other hand, when dealing with real data, normally, only one or two WTs are tested. For example, [9] where support vector machines are used to predict WT faults, and [10], where different machine learning classifiers are compared to predict generator faults. These references use real WT SCADA data from one and two WTs, respectively, and are based on supervised approaches that require historical fault data to be constructed. This is an important drawback, as obtaining labeled datasets from operational data is typically hard, it is exposed to errors, and leads to a highly unbalanced dataset.