Summary: Residual life time distribution prediction of products is of great importance in maintenance, replacement and spare parts decision making. The majority of existing methods use only the degradation data of the products themselves, so the precision of the prediction results is hardly satisfying when the degradation data are few. This paper tackles the problem using a Bayesian method. The degradation data of the products being a Wiener process with random effect are modeled. Using the Bayesian method, featuring online degradation data and historical life time data are fused to derive the posterior distribution and Bayesian estimates of the degradation parameters. The residual life time distribution is deduced, thus improving the predictive precision. An example, residual life time distribution prediction of metallized film pulse capacitors, is presented to show the validity of the proposed method.