Bearings are important parts in machinery products. Their performance and life are closely related to operating lives of mechanical systems. It is necessary to consider the effects of different failure modes on rolling bearings’ reliability. Here, aiming at the full failure data in bearings’ reliability tests, the prior distributions of bearing life distribution parameters were established with Bootstrap method. Then, the corresponding posterior distributions were estimated using Bayes method. The bearing life distribution parameters were obtained through the posterior expectation reduction. The life distribution model of bearing local failure was gained through further analyzing bearings’ vibration performance degradation data. Copula function was used to analyze comprehensively the life distribution model of bearing full failure and that of bearing local failure. The relative parameters of Copula function were estimated with the experience Kendall relative ranks of test data. Finally, the reliability assessment results of bearings under competing failure were achieved. The results were helpful to find defects in bearing design and improve the bearing reliability.