The detection of generator bearing failures on wind turbines using machine learning based anomaly detection

In this research an early warning methodological framework is developed that is able to detect premature failures due to excessive wear. The methodology follows the data-driven Normal Behavior Model (NBM) principle, in which one or more data-driven models are used to model the normal behavior of the wind turbine. Anomalous behaviour of the turbine is identified by analyzing the deviation between the observed and predicted normal behaviour. The framework consists of two pipelines, a statistics and machine learning based pipeline. The former is based on techniques like ARIMA, OLS and CUSUM. The latter makes use of techniques like Random Forest, Gradient Boosting, … Each pipeline has its strengths and weaknesses, but by combining them in an intelligent way, a more capable detector is developed. The methodology is validated on 10-minute SCADA data from a real operational wind farm. The validation case focuses on generator (front/rear) bearing failures. The goal is to predict these failures well in advance (ideally at least a month) using the developed framework, which should allow for timely adjustments to the maintenance plan. The results show that the methodology is able to accomplish this reliably.