Remaining lifetime prediction is of vital importance in equipment maintenance and repairment.Most of the remaining lifetime prediction methods now available only use the current degradation information of equipment,but does not take full advantage of the historical lifetime information,while this information always contains the evolution information of equipment lifetime,which is of great significance for accurate predicting of the remaining lifetime.To solve this problem,a remaining lifetime prediction method is proposed by integrating the stochastic degradation process with hazard rate.Firstly,the equipment degradation process is modeled as a Wiener process,and then Cox proportional hazard model is used for modeling,which integrates the influence of equipment degradation process on the hazard rate,thus attaining the purpose for using the historical monitoring information.Furthermore,parameters of the degradation model are updated with the current degradation monitoring information by Bayesian method,based on which,the remaining lifetime can be predicted,hence realizing effective fusion of equipment historical data and current data.Finally,the proposed method is verified by the degradation measurement data of laser generator.It is demonstrated that the proposed method is valid,with certain application value.