Health prediction of rolling bearing based on digital twin

The health status of rolling bearings largely determines the reliability level of mechanical equipment. The health state prediction of rolling bearings can help the safe operation of mechanical equipment. Therefore, this paper proposes a real-time prediction method of the remaining life of rolling bearings based on digital twin. Firstly, the method is based on digital twin technology means to obtain real-time sensing information of rolling bearings, so as to establish a digital twin model of rolling bearings considering real-time working condition changes. Secondly, a remaining life prediction model considering the measurement error is established by non-linear Brownian motion. Then, the unknown parameters in the model are solved by using the great likelihood estimation method, and the parameters are updated in real time by using Bayesian theory, so that the remaining life of the rolling bearing can be predicted in real time. Finally, the feasibility and effectiveness of the method are verified by analyzing the information of the whole life cycle of rolling bearings.