The equipment Remaining Useful Lifetime (RUL) prediction method based on dropout Neural Network (dropout NN) has low precision since it uses a Bayesian Neural Network (BNN) with fixed priori distribution and approximate posterior distribution.To solve the problem, we proposed a RUL prediction method based on BNN with Gaussian approximation posterior distribution and a RUL prediction method based on mixed Gaussian-Bernoulli network.The former introduces the mixed Gaussian distribution as priori distribution and then optimizes BNN by unbiased Monte Carlo estimation of parameter gradient, while the latter introduces a discretized Gaussian prior distribution to define KL divergence correctly, and then optimizes the BNN.The verification results on PHM 2012 bearing dataset show that the mixed Gaussian-Gaussian network has better effect than dropout NN, which proves that BNN with the changed distribution combination can obtain better prediction effect.