Research on rolling bearing virtual-real fusion life prediction with digital twin

• Published in: Mechanical systems and signal processing 2023-09, Vol.198, p.110434, Article 110434
• Main Authors: Zhao, Wentao, Zhang, Chao, Fan, Bin, Wang, Jianguo, Gu, Fengshou, García Peyrano, Oscar, Wang, Shuai, Lv, Da
• Format: Article
• Language: English
• Subjects: CycleGAN; Digital twin; Remaining useful life prediction; Rolling bearing; Small sample
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2023.110434

[Display omitted] •Application of digital twin in life prediction of mechanical equipment.•Digital twin provide rich and independent virtual simulation data.•The improved CycleGAN can map simulation data in virtual space to measured data in physical space.•Large sample data can significantly improve the accuracy of various life prediction models. The prediction accuracy of the remaining useful life of rolling bearings is greatly affected by the size of sample data, and it is difficult to obtain enough fault samples in practical applications. Digital twin technology can reproduce the operation process of rolling bearings and other mechanical equipment in the digital world, which provides a new paradigm for life prediction under the condition of small samples. In this paper, a virtual and real combination of life-cycle rolling bearing digital twin is proposed. The modified CycleGAN combined with Wasserstein distance is used to map the simulation data in virtual space to the measured data in physical space, which significantly reduces the error between the simulation data and the measured data. The effectiveness of the improved rolling bearing digital twin and the availability of simulation data are verified by experiments. The simulation data are applied to the advanced remaining useful life prediction method, and the high-precision life prediction of rolling bearings is realized. The comparison with the traditional life prediction method verifies that the proposed method can effectively solve the small sample problem.