Bo Xie


  1. [1] Y. Tian, K. Zhang, B. Jiang, and X.G. Yan, Interval observerand unknown input observer-based sensor fault estimation forhigh-speed railway traction motor, Journal of the FranklinInstitute, 357(2), 2020, 1137–1154.
  2. [2] G. Lin and S. Gao, Selective-tripping scheme for powersupply arm on high-speed railway based on correlation analysisbetween feeder current fault components in multisite, IEEJTransactions on Electrical and Electronic Engineering, 14(5),2019, 773–779.
  3. [3] D.V. Shevchenko, Methodology for constructing digital twinsin railway transport, Vestnik of the Railway Research Institute,80(2), 2021, 91–99.
  4. [4] E.Y. Ahn, Analysis of digital twin technology trends relatedto geoscience and mineral resources after the Korean new dealpolicy in 2020, Economic and Environmental Geology, 54(6),2021, 659–670.
  5. [5] G. Magorzata, Digital twin technology: Awareness, implemen-tation problems and benefits, Engineering Management inProduction and Services, 14(1), 2022, 63–77.
  6. [6] S.Y. Chen, T.H. Wang, X.M. Li, and L. Zhu, Research on theimprovement of teachers’ teaching ability based on machinelearning and digital twin technology, Journal of Intelligent &Fuzzy Systems: Applications in Engineering and Technology,40(4), 2021, 7323–7334.
  7. [7] Y. Zheng, X. Xue, and J. Zhang, Research on fault diagnosis ofhydraulic system of fast erecting device based on fuzzy neuralnetwork, International Journal of Fluid Power, 23(2), 2022,141–159.
  8. [8] H. Li, J. Huang, J. Huang, S. Chai, L. Zhao, and Y. Xia,Deep multimodal learning and fusion based intelligent faultdiagnosis approach, Journal of Beijing Institute of Technology,30(2), 2021, 172–185.
  9. [9] J. Li, M. Lin, Y. Li, and X. Wang, Transfer learning withlimited labeled data for fault diagnosis in nuclear power plants,Nuclear Engineering and Design, 390, 2022, 62–74.
  10. [10] Z. Yang, Y. Shen, R. Zhou, and F. Yang, A transferlearning fault diagnosis model of distribution transformerconsidering multi-factor situation evolution, IEEJ Transactionson Electrical and Electronic Engineering, 15(1), 2019,30–39.
  11. [11] K. Zhao, H. Jiang, Z. Wu, and T. Lu, A novel transferlearning fault diagnosis method based on manifold embeddeddistribution alignment with a little labeled data, Journal ofIntelligent Manufacturing, 33(1), 2022, 151–165.
  12. [12] X. Shi, X. Zhu, and J. Zhang, Simulation and experimentalanalysis of drive motor stator current under local fault of gear,Mechatronic Systems and Control, 49(2), 2021, 74–82.
  13. [13] Y. Jing, X. Wang, Z. Yu, C. Wang, Z. Liu, and Y. Li, Diagnosticresearch for the failure of electrical transformer winding basedon digital twin technology, IEEJ Transactions on Electricaland Electronic Engineering, 17(11), 2022, 1629–1636.
  14. [14] W. Liu, L. Han, and L. Huang, Design and optimization ofmolten salt reactor monitoring system based on digital twintechnology, Kerntechnik, 87(6), 2022, 651–660.
  15. [15] K. Zhou, S. Yang, Z. Guo, and X. Long, J. Hou, and T.Jin, Design of automatic spray monitoring and tele-operationsystem based on digital twin technology, Proceedings of theInstitution of Mechanical Engineers, Part C: Journal ofMechanical Engineering Science, 235(24), 2021, 7709–7725.
  16. [16] Z. Chen, X. Yang, B. Jin, and M. Guo, Industrial internetsecurity evaluation technology based on digital twin, Journalof Computational Methods in Sciences and Engineering, 22(6),2022, 1981–1994.
  17. [17] D.B. Deebak and F. Al-Turjman, Digital-twin assisted: Faultdiagnosis using deep transfer learning for machining toolcondition, International Journal of Intelligent Systems, 37(12),2022, 10289–10316.
  18. [18] S.R. Kenett and J. Bortman, The digital twin in Industry 4.0:A wide-angle perspective, Quality and Reliability EngineeringInternational, 38(3), 2022, 1357–1366.
  19. [19] F. Smarandache, Plithogeny, plithogenic set, logic, probabilityand statistics: A short review, Journal of Computational andCognitive Engineering, 1(2), 2022, 47–50.8
  20. [20] R. Arunthavanathan, F. Khan, S. Ahmed, and S. Imtiaz,Autonomous fault diagnosis and root cause analysis for theprocessing system using one-class SVM and NN permutationalgorithm, Industrial & Engineering Chemistry Research,61(3), 2022, 1408–1422.
  21. [21] X. Song, Y. Cong, Y. Song, Y. Chen, and P. Liang, A bearingfault diagnosis model based on CNN with wide convolutionkernels, Journal of Ambient Intelligence and HumanizedComputing, 13(8), 2022, 4041–4056.
  22. [22] X. Zhou, X. Xu, J. Zhang, L. Wang, D. Wang, and P. Zhang,Fault diagnosis of silage harvester based on a modified randomforest, Information Processing in Agriculture, 10(3), 2023,301–311.

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