There is no doubt that remaining useful life prediction is important to the health management of modern equipment. Particle filter method has been widely applied to the prediction of equipment remaining useful life in recent years, because it can solve the filtering problem of nonlinear and non-Gaussian systems better and it allows the uncertainty management. However, the prediction performance of a particle filter method is largely dependent on the prediction model and is very sensitive to the initial distribution of the model parameters. These flaws limit the further development of particle filter methods in the prediction to a certain extent. Aiming at the shortcomings of the basic particle filter prediction method, a kind of general prediction framework based on degradation rate tracking particle filter was presented. In the proposed method, the statistical rule of historical data was utilized to guide the degradation rate tracking of target data and simplify the prediction process. The remaining useful life prediction cases of rolling bearings and Li-ion battery verified the effectiveness of the proposed method.