Life extension of wind turbine drivetrains by means of SCADA data: Case study of generator bearings in an onshore wind farm

Accurate predictions for remaining useful life (RUL) of wind turbine drivetrains are crucial in reducing downtime, optimising maintenance strategies, extending operational life and improving costs. The purpose of this study is to present a method which utilises SCADA data for RUL estimation. It aims to answer whether, by only having the basic SCADA data, normally collected from all wind turbines, valuable RUL prediction results can be obtained. The work intends to develop a reliable, accurate, user-friendly and informative tool to enable observation of any growing trends in the proposed metrics, such as temperature difference, cumulative sum of temperature difference and the moving average of the cumulative sum. These metrics are defined based on the differences between the actual temperatures of the components, that might have undergone some damage and the model predicted temperatures of those components if they would have remained healthy, throughout years of operation. A machine learning model is used, along with selected SCADA input parameters, to predict the healthy state temperature of components. The proposed method is implemented on SCADA data collected from an actual wind farm over seven years. The results of this study show that while the SCADA data analysis can contribute to fault detection, the estimation of RUL purely based on SCADA appears to be uncertain. Such tools however, can be used as a monitoring method, during operation, to record abnormality trend of the components over the years and can be used as important inputs for the life extension evaluation of wind turbine drivetrains.