Abstract: Due to existing machine learning methods that fusing SCADA time series data without time memory capability may lead to low accuracy for wind turbine gearbox condition prediction,a model based on a LSTM neural network fusing SCADA data is proposed to solve this problem. Firstly,selecting one monitoring parameter which can reveal the working operation condition of gearbox as the model output,and grey correlation analysis method is used to select SCADA parameters closely related to the monitoring parameter as the model inputs. Then,the LSTM prediction model is established using healthy working condition data to calculate the prediction values and residuals. Finally,the upper and lower thresholds to monitor the condition of wind turbine are calculated based on the three sigma rule. The results of experiment adopted the measured SCADA data of a wind farm show that the proposed model can effectively realize the wind turbine gearbox fault warning.