Data-Model Combined Driven Digital Twin of Life-Cycle Rolling Bearing
Yi Qin, Xingguo Wu, Jun Luo
Abstract
The digital twin of a life-cycle rolling bearing is significant for its degradation performance analysis and health management. This article proposes a digital twin model of life-cycle rolling bearing driven by the data-model combination. With the measured signals and the bearing fault dynamic model, the time-varying defect size is estimated, and the evolution law of bearing defect during the life cycle is revealed by a back propagation neural network. Then, the excitations of evolutionary defects are introduced into the bearing dynamic model, so as to form a life-cycle bearing dynamic model in the virtual space. Finally, the simulation data in the virtual space is mapped into the corresponding data in the physical space via an improved CycleGAN neural network with the smooth cycle consistency loss. By comparing the obtained digital twin result with the measured signal in the time-domain and frequency-domain, the effectiveness of the proposed model is verified.