Open Access
Issue
Int. J. Metrol. Qual. Eng.
Volume 17, 2026
Article Number 11
Number of page(s) 18
DOI https://doi.org/10.1051/ijmqe/2026009
Published online 18 June 2026
  1. I. Sabahi, M. Vermaut, M. Kirchner, Z. Li, K. Gryllias, F. Naets, Bearing housing as a sensor: estimation of lumped bearing loads using accurate housing models, Mech. Syst. Signal Process. 242, 113591 (2026) [Google Scholar]
  2. J. Koutsoupakis, D. Giagopoulos, A simulation-enhanced deep learning framework for bearing fault diagnosis using an improved bearing force model, Mech. Syst. Signal Process. 241, 113519 (2025) [Google Scholar]
  3. C. Deng, J. Lu, L. An, Z. Gao, X. Cheng, C. Tian, A friction temperature model for dynamic operating bearing based on CNN and CNNLSTM, Eng. Appl. Artif. Intell. 160, 112042 (2025) [Google Scholar]
  4. D. He, J. Zhao, Z. Jin, C. Huang, F. Zhang, J. Wu, Prediction of bearing remaining useful life based on a two-stage updated digital twin, Adv. Eng. Inform. 65, 103123 (2025) [Google Scholar]
  5. X. Ma, Z. Li, J. Xiang, X. Sun, C. Chen, F. Huang, Nonlinear vibration analysis of rotor-bearing system with insufficient interference and bearing tilt, Int. J. Mech. Sci. 287, 109966 (2025) [Google Scholar]
  6. R. Zhang, C. Song, Y. Zhou, J. Tan, Z. Zeng, Tribodynamic behavior of a novel double bearing layout for the main shaft system of wind turbines, Tribol. Int. 204, 110496 (2025) [Google Scholar]
  7. L. Wang, X. Ding, X. Liu, J. Cai, R. Li, Optimization and multiphysics coupling analysis of high bearing capacity for wind turbine gearbox sliding bearings, Tribol. Int. 214, 111162 (2026) [Google Scholar]
  8. Q. Zhao, X. Zhang, J. Xie, S. Liang, E. Mbeka, KFC-Former: an interpretable kernel function convolutional-former with phase space reconstruction for remaining useful life prediction of mechanical equipment, Expert Syst. Appl. 302, 130472 (2026) [Google Scholar]
  9. Q. Li, X. Xiong, J. Ren, Remaining useful life prediction based on degradation-aware dynamic graph representation learning, Eng. Appl. Artif. Intell. 163, 113140 (2026) [Google Scholar]
  10. Y. Yuan, Y. Han, K. Xiao, Z. Xu, X. Jiang, Remaining useful life prediction for the harmonic reducer of industrial robots via in-situ current signal and lightweight multiscale attention deep networks, J. Manuf. Syst. 83, 322–336 (2025) [Google Scholar]
  11. T. Xia, X. Xing, T. Yan, D. Wang, E. Pan, L. Xi, Interpretable temporal degradation state chain based fusion graph for intelligent bearing fault detection, Adv. Eng. Inform. 59, 102342 (2024) [Google Scholar]
  12. A. Cao, H. Gao, S. Fan, L. Guo, Z. You, Y. Lei, Y. Sun, J. He, Study on vibration mechanism and dynamic characteristics for TBM main bearing defects, Mech. Syst. Signal Process. 222, 111807 (2025) [Google Scholar]
  13. Z. Yin, F. Zhang, C. Yin, G. Xu, S. Liu, A bearing fault diagnosis method for sample imbalance, Eng. Appl. Artif. Intell. 157, 111171 (2025) [Google Scholar]
  14. S. Wu, P. Shi, X. Xu, X. Yang, R. Li, Z. Qiao, KMDSAN: a novel method for cross-domain and unsupervised bearing fault diagnosis, Knowl.-Based Syst. 312, 113170 (2025) [Google Scholar]
  15. D. He, J. Zhao, Z. Jin, C. Huang, C. Yi, J. Wu, DCAGGCN: a novel method for remaining useful life prediction of bearings, Reliab. Eng. Syst. Saf. 260, 110978 (2025) [Google Scholar]
  16. Y. Du, X. Geng, Q. Zhou, S. Cheng, H. Zhang, Dynamic preventive maintenance model for offshore wind turbine bearings based on remaining useful life prediction, Sustain. Energy Technol. Assess. 84, 104750 (2025) [Google Scholar]
  17. R. Guo, J. Yi, X. Luo, An efficient classification and error correction knowledge distillation framework for remaining useful life prediction of bearings in air turbine starter, Neurocomputing 657, 131611 (2025) [Google Scholar]
  18. G. Li, J. Wei, J. He, H. Yang, F. Meng, Implicit Kalman filtering method for remaining useful life prediction of rolling bearing with adaptive detection of degradation stage transition point, Reliab. Eng. Syst. Saf. 235, 109269 (2023) [Google Scholar]
  19. X. Si, H. Li, Z. Zhang, N. Li, A Wiener-process-inspired semi-stochastic filtering approach for prognostics, Reliab. Eng. Syst. Saf. 249, 110200 (2024) [Google Scholar]
  20. Y. Jin, D. Liu, Y. Xiao, L. Cui, Dual-channel dynamic spline graph convolutional network for bearing remaining useful life prediction, Reliab. Eng. Syst. Saf. 266, 111731 (2026) [Google Scholar]
  21. W. Yin, H. Xia, E. Zio, X. Huang, Deep ensemble learning and error correction method for remaining useful life prediction of rolling bearings, Eng. Appl. Artif. Intell. 161, 112128 (2025) [Google Scholar]
  22. Z. Pan, X. Pan, F. Jiang, Y. Wu, Z. Meng, Y. Wang, P. Zhao, W. Lu, Remaining useful life prediction of rolling-element bearings based on cumulative transformation and EResNet-KSLSTM network, Mech. Syst. Signal Process. 238, 113251 (2025) [Google Scholar]
  23. M. Furqon, M. Pratama, L. Liu, H. Habibullah, K. Dogancay, Mixup domain adaptations for dynamic remaining useful life predictions, Knowl.-Based Syst. 295, 111783 (2024) [Google Scholar]
  24. J. Shang, D. Xu, X. Shang, C. Zhao, C. Jiang, H. Qiu, L. Gao, CITDG: a causality and information-theory inspired domain generalization method for machine remaining useful life prediction in unseen domains, Mech. Syst. Signal Process. 241, 113527 (2025) [Google Scholar]
  25. J. Du, L. Song, X. Gui, J. Zhang, L. Guo, X. Li, Remaining useful life prediction under variable operating conditions via multisource adversarial domain adaptation networks, Appl. Soft Comput. 161, 111717 (2024) [Google Scholar]
  26. C. Wu, C. Ma, J. He, C. Fang, L. Wang, X. Jin, Y. Liu, Consider the sample-weighted multi-source partial domain adaptation network for remaining useful life prediction under incomplete target data, Adv. Eng. Inform. 68, 103775 (2025) [Google Scholar]
  27. H. Tian, J. Mi, S. Tong, Y.-F. Li, Uncertainty-weighted with gradient-based to re-weight domain generalization for remaining useful life prediction of rotating machinery under unseen conditions, Reliab. Eng. Syst. Saf. 267, 111810 (2026) [Google Scholar]
  28. W. Mao, R. Guo, J. Wang, M. Zuo, Z. Zhong, Wiener process-assisted online remaining useful life prediction with deep incremental regression transfer learning, Reliab. Eng. Syst. Saf. 267, 111867 (2026) [Google Scholar]
  29. T. Liu, Y. Li, Z. Hu, C. Fu, G. Song, Remaining useful life prediction of bearings based on small-sample enhanced and interpretable transfer learning, Adv. Eng. Inform. 69, 103938 (2026) [Google Scholar]
  30. Z. Men, D. Gong, K. Zhou et al., Unsupervised domain adaptation method for bearing fault diagnosis assisted by twin data under extreme sample scarcity, Mech. Syst. Signal Process. 239, 113359 (2025) [Google Scholar]
  31. Z. Men, Y. Li, L. Gao et al., Fault diagnosis method for railway wagon bearings under imbalanced dataset based on improved ACWGAN, Nonlinear Dyn. 113, 14935–14962 (2025) [Google Scholar]
  32. J. Zhao, D. He, Z. Jin, X. Zhang, J. Zhou, A new method for bearing remaining useful life prediction based on dynamic wavelet and physical information constraints, Expert Syst. Appl. 296, 129023 (2026) [Google Scholar]
  33. B. Wang, Y. Lei, N. Li, N. Li, A hybrid prognostics approach for estimating remaining useful life of rolling element bearings, IEEE Trans. Reliab. 69, 401–412 (2020) [Google Scholar]
  34. Y. Zhang, X. Zhao, Z. Peng, R. Xu, Y. Hui, Cross-domain remaining useful life prediction for rolling bearings based on wavelet decomposition and dynamic calibrated domain adaptive networks, Measurement 251, 117278 (2025) [Google Scholar]
  35. H. Cheng, X. Kong, G. Chen, Q. Wang, R. Wang, Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors, Measurement 168, 108286 (2021) [Google Scholar]
  36. T. Hu, Y. Guo, L. Gu, Y. Zhou, Z. Zhang, Z. Zhou, Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method, Reliab. Eng. Syst. Saf. 219, 108265 (2022) [Google Scholar]
  37. M. Ragab, Z. Chen, M. Wu, C.S. Foo, C.K. Kwoh, R. Yan, X. Li, Contrastive adversarial domain adaptation for machine remaining useful life prediction, IEEE Trans. Ind. Inform. 17, 5239–5249 (2021) [Google Scholar]
  38. S. Xie, W. Cheng, Z. Nie, J. Xing, X. Chen, L. Gao, Z. Xu, R. Zhang, Multidimensional attention domain adaptive method incorporating degradation prior for machine remaining useful life prediction, IEEE Trans. Ind. Inform. 20, 7345–7356 (2024) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.