- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | ZHANG Ke, HONG Yang, GAO Tianhao, BAI Xu, WANG Nan, SHI Huaitao, LONG Yanze. Digital Twin-Based Life Prediction of Full-Ceramic Rolling Bearings[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20250347
|
To address two challenges in the health monitoring of full-ceramic rolling bearings, namely the difficulty in accurately characterizing the crack propagation mechanism and the limited accuracy of life prediction caused by the scarcity of monitoring data samples under extreme operating conditions, a hybrid network model for high-precision life prediction under small-sample conditions was developed. A two-degree-of-freedom dynamic model was established based on bearing operating characteristics to analyze and describe the nonlinear dynamic behavior of the bearing through vibration responses. Time-domain statistical and frequency-domain energy features were combined to conduct a joint characterization of state information. By considering the characteristic that the crack propagation process of brittle materials is difficult to directly observe, physical constraints governing stable crack propagation were integrated into the physics-informed neural network training, ensuring simulated signal evolution aligned with actual crack propagation trends. A generative adversarial neural network mapped simulated signals to real signals, producing high-fidelity simulated vibration data to expand the training sample space and alleviate small-sample limitations. A random forest regression model was applied for life prediction and identification of critical crack instability states, forming a complete analysis process of residual life assessment for ceramic bearings. Results indicate that the method accurately captures the laws of dynamic evolution from crack initiation to propagation, achieving an average life prediction accuracy of over 99.5%. The simulated signals fused with physical constraints show high consistency with real signals in the energy distribution of key feature frequency bands and peak locations of the envelope spectrum. This method improves the capability of identifying crack propagation trends and enables the recognition of the critical node corresponding to the transition from stable to unstable crack propagation.
| [1] |
Tian J X, Wu Y H, Sun J, et al. Thermal dynamic exploration of full-ceramic ball bearings under the self-lubrication condition[J]. Lubricants, 2022, 10(9): 213. doi: 10.3390/lubricants10090213
|
| [2] |
Baranger E, Cluzel C, Ladevèze P, et al. Effects of the thermomechanical loading path on the lifetime prediction of self-healing ceramic matrix composites[J]. Science and Engineering of Composite Materials, 2011, 18(4): 259-264 doi: 10.1515/secm.2011.050
|
| [3] |
Sun Y, Xie H, Liu L J, et al. Enhanced mechanical properties in ceramic multilayer composites through integrating crystallographic texture and second-phase toughening[J]. Ceramics International, 2021, 47(22): 31222-31228. doi: 10.1016/j.ceramint.2021.07.298
|
| [4] |
王彪, 秦勇, 贾利民, 等. 监测数据驱动的城轨列车轴箱轴承剩余寿命预测[J]. 西南交通大学学报, 2024, 59(1): 229-238. doi: 10.3969/j.issn.0258-2724.20220230
Wang Biao, Qin Yong, Jia Limin, et al. Monitoring data-driven prediction of remaining useful life of axle-box bearings for urban rail transit trains[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 229-238. doi: 10.3969/j.issn.0258-2724.20220230
|
| [5] |
Kunzelmann B, Rycerz P, Xu Y L, et al. Prediction of rolling contact fatigue crack propagation in bearing steels using experimental crack growth data and linear elastic fracture mechanics[J]. International Journal of Fatigue, 2023, 168: 107449. doi: 10.1016/j.ijfatigue.2022.107449
|
| [6] |
Khader I, Rasche S, Lube T, et al. Lifetime prediction of ceramic components–A case study on hybrid rolling contact[J]. Engineering Fracture Mechanics, 2017, 169: 292-308. doi: 10.1016/j.engfracmech.2016.10.014
|
| [7] |
Wu B, Zhang X H, Shi H, et al. Failure mode division and remaining useful life prognostics of multi-indicator systems with multi-fault[J]. Reliability Engineering & System Safety, 2024, 244: 109961. doi: 10.1016/j.ress.2024.109961
|
| [8] |
Salahi Nezhad M, Larsson F, Kabo E, et al. Numerical prediction of railhead rolling contact fatigue crack growth[J]. Wear, 2023, 530/531: 205003.
|
| [9] |
Sevener K M, Tracy J M, Chen Z, et al. Crack opening behavior in ceramic matrix composites[J]. Journal of the American Ceramic Society, 2017, 100(10): 4734-4747. doi: 10.1111/jace.14976
|
| [10] |
Li J P, Zhou Y, Lu Z C, et al. Experimental study on the mechanism of wheel-rail steels crack initiation and wear growth under rolling contact fatigue[J]. Procedia Structural Integrity, 2022, 37: 582-589. doi: 10.1016/j.prostr.2022.01.126
|
| [11] |
Vetrone J, Obregon J E, Indacochea E J, et al. The characterization of deformation stage of metals using acoustic emission combined with nonlinear ultrasonics[J]. Measurement, 2021, 178: 109407. doi: 10.1016/j.measurement.2021.109407
|
| [12] |
Li T, Shi H T, Bai X T, et al. A digital twin model of life-cycle rolling bearing with multiscale fault evolution combined with different scale local fault extension mechanism[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3507211. doi: 10.1109/tim.2023.3243663
|
| [13] |
Li S, Jiang Q B, Xu Y D, et al. Digital twin-driven focal modulation-based convolutional network for intelligent fault diagnosis[J]. Reliability Engineering & System Safety, 2023, 240: 109590. doi: 10.1016/j.ress.2023.109590
|
| [14] |
Ying J, Yang Z J, Chen C H, et al. Remaining useful life prediction of rolling element bearing based on hybrid drive of data-driven and dynamic model[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2022: 09544062221141333.
|
| [15] |
Zhao W T, Zhang C, Fan B, et al. Research on rolling bearing virtual-real fusion life prediction with digital twin[J]. Mechanical Systems and Signal Processing, 2023, 198: 110434. doi: 10.1016/j.ymssp.2023.110434
|
| [16] |
Ye Y M, Yang Q, Yang F, et al. Digital twin for the structural health management of reusable spacecraft: a case study[J]. Engineering Fracture Mechanics, 2020, 234: 107076. doi: 10.1016/j.engfracmech.2020.107076
|
| [17] |
Qin Y, Wu X G, Luo J. Data-model combined driven digital twin of life-cycle rolling bearing[J]. IEEE Transactions on Industrial Informatics, 2022, 18(3): 1530-1540. doi: 10.1109/TII.2021.3089340
|
| [18] |
Zhang R, Zeng Z Q, Li Y F, et al. Research on remaining useful life prediction method of rolling bearing based on digital twin[J]. Entropy, 2022, 24(11): 1578. doi: 10.3390/e24111578
|
| [19] |
Lu Q B, Li M. Digital twin-driven remaining useful life prediction for rolling element bearing[J]. Machines, 2023, 11(7): 678. doi: 10.3390/machines11070678
|
| [20] |
An D, Shi K, Shao M, et al. Research on full-life rolling bearing virtual-real fusion defect prediction with digital twin[J]. Engineering Research Express, 2025, 7(2): 025538. doi: 10.1088/2631-8695/adb7b9
|
| [21] |
Cai Z X, Liu Y W, Zhang W, et al. A rolling bearing fault diagnosis method based on generalized dataset realizing small sample transfer learning[C]//2023 6th International Conference on Robotics, Control and Automation Engineering (RCAE). Suzhou, China. IEEE, 2023: 416-424.
|
| [22] |
Cui L L, Xiao Y C, Liu D D, et al. Digital twin-driven graph domain adaptation neural network for remaining useful life prediction of rolling bearing[J]. Reliability Engineering & System Safety, 2024, 245: 109991. doi: 10.1016/j.ress.2024.109991
|
| [23] |
Cao W, Meng Z, Li J M, et al. A remaining useful life prediction method for rolling bearing based on TCN-transformer[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3501309. [22] Cao W, Meng Z, Li J M, et al. A remaining useful life prediction method for rolling bearing based on TCN-transformer[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3501309.
|
| [24] |
Wang Q S, Sun Z C, Zhu Y M, et al. A fault diagnosis method based on an improved diffusion model under limited sample conditions[J]. PLoS One, 2024, 19(9): e0309714. [23] Wang Q S, Sun Z C, Zhu Y M, et al. A fault diagnosis method based on an improved diffusion model under limited sample conditions[J]. PLoS One, 2024, 19(9): e0309714.
|
| [25] |
Cai Z X, Liu Y W, Zhang W, et al. A rolling bearing fault diagnosis method based on generalized dataset realizing small sample transfer learning[C]//2023 6th International Conference on Robotics, Control and Automation Engineering (RCAE). Suzhou, China. IEEE, 2023: 416-424.
|
| [26] |
孟春成, 亐道远, 段晓晨. 城市轨道交通土建工程造价非线性预测与反演[J]. 西南交通大学学报, 2025, 60(1): 137-146. doi: 10.3969/j.issn.0258-2724.20230209
Meng Chuncheng, Yu Daoyuan, Duan Xiaochen. Nonlinear prediction and inversion of civil engineering cost of urban rail transit[J]. Journal of Southwest Jiaotong University, 2025, 60(1): 137-146. doi: 10.3969/j.issn.0258-2724.20230209
|
| [27] |
易彩, 林建辉, 汪浩, 等. VMD引导的轮对与轴承复合故障诊断方法[J]. 西南交通大学学报, 2024, 59(1): 151-159. doi: 10.3969/j.issn.0258-2724.20211088
Yi Cai, Lin Jianhui, Wang Hao, et al. Compound fault diagnosis method guided by variational mode decomposition for wheelsets and bearings[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 151-159. doi: 10.3969/j.issn.0258-2724.20211088
|
Figures(20) / Tables(3)
DownLoad:
