# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 1 Introduction
Additionally, the methodology can not be applied straightforward to wind parks where the fault of interest did not occur in the past. Thus, despite the promising performance of supervised methods, unsupervised approaches are preferred for SCADA predictive maintenance [11]. The recent works related to unsupervised SCADA based predictive maintenance can be mainly grouped in one of the following three categories: signal trending, normality models, and anomaly detection models. In the following paragraphs, a brief review of each one of these categories is given. The approaches in the signal trending category study changes and trends in the SCADA time series. Some relevant works include [12], where monitoring of WT generators using the statistical inertia of a wind park average is proposed; and [13], where the difference between the SCADA data of each turbine with the median of the rest of turbines in the wind park is used to establish a condition vector to later apply vector autoregression Hotelling and vector autoregression multivariate exponentially weighted moving average to locate the faulty turbine. The main advantages of signal trending methods are its simplicity and interpretability. However, most of these methods can not capture complex relations among different variables, thus machine and deep learning models are needed to move beyond signal trending solutions. Regarding data-based normality models, also called normal behavior models, their objective is to learn the relation between a set of input variables and a target variable under normal operation. Then, the difference between predictions and real measurements (for the target variable) is used to detect abnormal behavior. This normality models are well established in unsupervised SCADA based predictive maintenance, but the general trend is to use the power curve (relation between the wind intensity and the extracted power) as the target variable. It is noteworthy the work of [14] where main working parameters (e.g., the rotor speed, and the blade pitch) are used as input variables and the power is employed as the target to construct the normality model. The main advantage of these methods is their capability to learn complex relations between different sensors. On the other hand, its major drawback is a lack of interpretability. The main bearing is the object of this analysis due to its central role in the drive train assemble. It connects the rotor to the gearbox, and it has to withstand the large torque generated by the rotor, making it prone to problems. Failure rates of 30% are reported for single main bearing and 15% for double main bearing calculated over a 20 year lifetime [15]. Within the typical wear mechanisms affecting the main bearing, micro-pitting, spalling, and smearing can be listed [16]. Main bearing failures can be anticipated through vibration analysis, but also using predictive models based on SCADA data and analysis of temperature signals, as reported in [17]. In this work, a normality model at WT level is selected, based in [18], where the target variable is selected to be the closest sensor to the component under study. In particular, as the main bearing is the component to be monitored, the main shaft temperature is used as target variable. In this work, the component under study is the main bearing because, as stated by the European Academy of Wind Energy (EAWE) [16], the wind industry has identified main bearing failures as a critical issue in terms of increasing WT reliability and availability, as they lead to major repairs with high replacement costs and long downtime periods. The methods in the category of anomaly detection models, also called outlier detection, seek to identify rare samples which raise suspicion by differing significantly from the majority of the data. A significant work is [19] where a comparison of three anomaly detection techniques (one-class support vector machine, isolation forest, and elliptical envelope) for WT CBM using SCADA data is realized.