Remaining useful life prediction of rolling bearings based on Bayesian neural network and uncertainty quantification

This paper constructs a remaining useful life (RUL) prediction model combining a convolutional neural network and a long short-term memory network (CNNLSTM) to support decision-making, especially the safety of rotational equipment. It avoids the influence of personnel and realizes the complementary advantages of the network. With the assistance of Bayesian short-term and long-term memory neural networks, the remaining life prediction method is able to provide the confidence interval of the remaining life prediction of rolling bearings. The compression between the proposed method and existing state-of-the-art methods validated the good performance of the proposed method. Overall, the proposed method contributes to life prediction and condition-based maintenance of bearings and complex rotational systems.