The degradation failure threshold is the important factor for the remaining useful lifetime prediction of equipment. Considering the current methods in which the influence of random failure threshold (RFT) can’t be considered, a real-time prediction method of remaining useful lifetime for equipment with RFT is proposed. Firstly, the nonlinear Wiener process with measurement error and random effect are used to model the degradation process. The maximum likelihood estimation (MLE) algorithm is used to estimate the parameters of degradation model and the distribution coefficients of RFT. Secondly, the probability density function (PDF) of remaining useful lifetime is derived by considering the RFT. Then, the parameter update method based on Bayesian theorem is presented to achieve the real-time prediction of remaining useful lifetime. Finally, the proposed method is used in the realtime prediction of gyroscope’s remaining useful lifetime and the results indicate that this method can effectively improve the precision and accuracy of remaining useful lifetime prediction.