Two-Stage Condition-based Maintenance Model of Wind Turbine: from Diagnosis to Prognosis

Due to the grossing share of wind turbines, tire challenges in the maintenance ph rang of already installed offshore and onshore wind farms are increasing and motivate the author to explore the risk analysis of key components of wind turbines. In this paper, a two-step condition-based maintenance model for wind turbines is pro is'. In the first stage, the socalled diagnostic stage, the normal behavior of these components was estimated through a tailor-made artificial neural network, Li the second stage, the pr tab stage, the deviation of the real-time measurement data from the estimated values was calculated. If the deviation increases beyond a confidence band, an alarm is triggered and a propi risk indicator is updated. By increasing the pmposed risk indkator, the corresponding anomaly is detected and condition-based maintenance programs can he planned. the effectiveneks of the model is demonstrated using real data from an Offshore wind farm in Germany. By implementing the proposed model using this rail-world data, it is shown that the proposed risk indicator is fully consistent with the upcoming wind turbine failures.