Digital twin based condition monitoring approach for rolling bearings

Digital twin is an important technology for grasping states of mechanical systems in real time. However, there are few studies on how to establish life-cycle digital twin models of bearings. In order to accurately estimate the condition of bearings, a digital twin model of bearing life cycle (BLDT) is proposed to achieve equivalent information on the virtual entity (VE) model and physical entity (PE) model. First, a dynamic model of rolling bearings and defect evolution model are established to simulate the dynamic response of the bearing performance degradation process. Then, the physical characteristics and degradation information of the PE model are exchanged with the VE model to evaluate the time-varying defect size and the equivalent comprehensive stiffness. The evolution law of the life-cycle is obtained through a neural network. Finally, the network parameters are introduced into the VE model to obtain dynamic response results of the life-cycle bearing dynamic model of other datasets under the same working conditions. By comparing the obtained digital twin results with experiment signals in the time and frequency domains, the accuracy and effectiveness of the BLDT model are verified.