For the problem of remaining useful lifetime (RUL) prediction of aero-engine, the present methods have not comprehensively considered the hidden degradation modeling and drift/diffusion coefficient synchronous updating. An online RUL prediction for aero-engine based on the condition monitoring (CM) data is presented in this paper. Firstly, the proportional degradation model of aero-engine is established based on the nonlinear Wiener process. Secondly, based on the historical condition monitoring data of similar engines, the degradation model parameters are estimated offline by using the maximum likelihood estimation (MLE) method. And then, based on the real-time condition monitoring data of the target engine, the drift/diffusion coefficient are synchronously update by using the Bayesian principle. Finally, the RUL probability density function of aero-engine is derived. The example analysis shows that the proposed method has higher prediction accuracy and precision than the traditional one, and has potential engineering application prospects.