Abstract：To capture the characteristics of dynamic temporal-spatial correlation hidden in monitoring data of wind turbine gearbox, a fault prediction method based on long short-term memory (LSTM) network is proposed. The proposed approach mainly consists of two phases: offline training and online detection. First, the oil temperature of the gearbox is taken as the modeling variable and the LSTM-based normal behavior model of wind turbine gearbox oil is built based on historical monitoring data training and learning, which fully takes advantages of the important correlated information between the oil temperature and some relevant input variables. Then, the model residuals are calculated and evaluated to determine the corresponding detection threshold. Furthermore, the well-trained LSTM model is used for online testing.Through model residual analysis and threshold comparison, fault detection and prediction of wind turbine gearbox can be realized.A real motoring data from a wind farm is used to validate the effectiveness of the proposed method. The results show that compared with those traditional methods, the proposed method presents better prediction performance, and can predict the occurrence of gearbox failure earlier.
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2020, Vol. 41 
