Abstract
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.
Original language | English |
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Article number | 110434 |
Number of pages | 21 |
Journal | Mechanical Systems and Signal Processing |
Volume | 198 |
Early online date | 11 May 2023 |
DOIs | |
Publication status | Published - 1 Sep 2023 |