Due to the lack of sensitivity and robustness to periodic fault shocks，the traditional degradation indicators are unable to track the degradation process of wind turbine bearings timely and predict remaining useful life accurately. In this paper， a real-time remaining useful life prediction method for wind turbine bearings based on the combination of envelope harmonic-to-noise ratio （EHNR） and unscented particle filter （UPF） is proposed. Firstly， the early degradation starting point of the bearing is detected by calculating the EHNR of the vibration signal and the trend characteristic of the EHNR is extracted as the novel degradation indicator. Secondly， the degradation model of bearing is constructed on the basis of historical data， and then the UPF algorithm is used to update the model parameters in order to realize the tracking and prediction of the bearing degradation stage. Finally， the actual monitoring data of wind turbine bearings is taken as an example to validate the proposed method， the results show that this method can start the life prediction mechanism in time and effectively solve the problem of particle degradation in traditional particle filter algorithm. Compared with commonly used support vector regression （SVR） and back propagation neural network（BPNN） prediction methods， it has higher prediction accuracy， and provides a reference for health management and reliability evaluation of large wind turbines.