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For the intelligent fault diagnosis algorithm of wind turbine gearbox (WTG), the isolated fault classification results often faced the question of confidence. In order to provide more information besides diagnosis conclusion while fault classification, based on the vibration time- frequency information, a two, stage framework for vibration time-frequency analysis and fault diagnosis of wind turbine gearbox was proposed. Firstly, at the first stage, the feature areas related to fault in the time frequency image were marked using the U-net model without manually setting the segmentation parameters. Then, the feature extraction method based on region shape feature was utilized to extract valuable information from the analyzed binary images. Finally, at the second stage, using these shape features based on region information, the random forest algorithm was used to complete the automatic fault identification of the WTGs. The proposed two stage pipeline was used to identify the fault conditions of in-service WTGs located in North China to verify its effectiveness. The experimental results show that the F1 score of the analysis algorithm reaches 0. 942, while the diagnostic accuracy of the diagnosis algorithm reaches 97. 4% . Comparing with existing methods, U-net method shows higher comprehensive diagnosis performance and computational efficiency. The results show that the proposed method can accurately mark the feature patches and feature bands in time frequency images and quickly and effectively diagnose the WTG faults. [ABSTRACT FROM AUTHOR]