# A Robust Wind Turbine Component Health Status Indicator
## 1 Introduction
Condition monitoring covers fault monitoring activities that can be performed using either signal-based or model-based techniques ([18]). In signal-based methods, in general, patterns are considered and the statistics of the signals are compared to threshold values. Then, the deviation between the thresholds and measurements is used as an indicator of abnormal behavior. In the model-based techniques, the model simulates the behavior of the component, and then the estimation of the signal is compared to real measurements. The resulting difference from this comparison is used as an indicator to detect the abnormal working of the investigated asset. The most straightforward approach to condition monitoring is asset status tracking via control charts ([19]), developed to monitor target variables easily. Generally, control charts are based on statistical parameters, through which the average historical behavior of the signal and significant intervals is defined. Via control charts, it is possible to detect normal and abnormal working trends and patterns of a signal. However, conventional control charts have some challenging prerequisites to fulfill, such that the collected data be independent and normally distributed. Therefore, control charts generate many false alarms under real operating conditions [20]. One of the solutions for these false alarms is to remove the auto-correlation which exists in input data. To do that, Exponentially Weighted Moving average techniques (univariate, EWMA, and multivariate, MEWMA) were implemented in the control chart design process [21]. The EWMA control charts were not able to detect the faults for wind turbines, while in comparison, the MEWMA control charts could detect the faults with a half an hour lead time window in [20]. The better performance of the MEWMA, compared to the EWMA, was associated with the multivariate nature of wind turbine faults. Nevertheless, a half an hour lead time window is very limited to schedule and/or perform maintenance activities. From the decision maker's perspective, this means simultaneously monitoring several signals together with the result that is not worth it. Therefore, there is room for improvement in existing anomaly detection techniques for wind turbines, which are multicomponent and multivariate entities, as the existing methods lack the consideration of real working conditions and adequate integration of available SCADA signals into the anomaly indicators [22]. The applicability of the existing models to the condition monitoring data is limited due to their short lead times between the alert status and the failure time. Furthermore, wind turbine faults require the analysis of multivariate systems, so selecting the variables to be considered and their relationships is essential. The selection of these variables is crucial for better model performance, but also for a better understanding of their operation and avoiding black-box models that are difficult to interpret physically. Depending on the component under investigation, the signals considered in model-based condition monitoring must be revised and selected carefully. In particular, oil temperature, rotational speed, and power signals obtained from SCADA and vibration and oil debris obtained from CMS are relevant variables to detect gearbox failures [23]. Notably, vibrations require special attention, evidenced by the comprehensive review conducted in [24] or the promising results presented in [25] based on generalized multiscale Poincare plots (GMPOP) and support vector data description (SVDD). Also, in the literature oil pressure was considered as an input variable for the gearbox failure detection. In the case of the generator, variables such as generator speed, active power, or reactive power are variables that can be studied for fault detection. In the case of the blades, other variables such as wind speed, rotor speed, or the miscorrelations between wind speed and torque shaft are parameters of interest for this purpose [26].