Fault warning and identification of front bearing of wind turbine generator

In order to realize the fault warning and identification of front bearing of wind turbine generator, in this paper a time-frequency domain modeling method is proposed, which integrates the time series data of supervisory control and data acquisition (SCADA) system with the vibration data of condition monitoring system (CMS). Firstly, the temperature model of generator front bearing based on gated recurrent unit (GRU) neural network is established using the SCADA data, and the temperature residual features are calculated. Secondly, the time domain features and frequency domain features of the vibration signal of generator front bearing are extracted. Finally, the temperature residual features and the time-frequency domain features of the vibration signal are fused, and the extreme gradient boosting (XGBoost) based fault identification model of the front bearing is established, which can identify five working conditions of the generator front bearing, including normal, inner ring damage, outer ring damage, shaft imbalance and rolling body damage. Extensive experiment results demonstrate that the proposed method can achieve higher identification accuracy compared with the front bearing fault warning identification method using the vibration signal characteristics alone. The average identification accuracy for normal, inner ring damage and outer ring damage conditions increase from 87%, 58.5% and 65% to 88.5%, 67.5% and 74%, respectively. © 2020, Science Press. All right reserved.