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Fuzzy rule-based neural network for high-speed train manufacturing system scheduling problem

  • S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021)
  • Published:
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Abstract

Our country has a vast territory, and rail transit is very important to the development of our country's national economy. In this paper, key technologies for a digital twin-based shop floor management and control system are investigated, and the concept is designed and implemented. By adding a digital twin between the business management layer and the production execution layer of the traditional workshop management and control system through the fuzzy rule neural network, a new workshop management and control system architecture on the basis of the virtual is formed, enabling intellectual management and control of the workshop. The results of the study found that the integration of the digital twin into the conventional shop floor management and control system led to changes in the composition, processes and information integration of the management and support system. For the purpose of comparing the system scheduling of the high-speed railway on the basis of the vague rule neural network with the traditional method, we made statistics on the system scheduling before and after the transformation. In terms of manufacturing volume, after the output exceeds 200, the speed of the traditional manufacturing method lags behind the fuzzy rule neural network by nearly 50%.

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References

  1. Lee J (2020) Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing. 38(8):901–910. https://www.sciencedirect.com/science/article/abs/pii/S2213846319300264

  2. Schroeder GN, Steinmetz C, Pereira CE et al (2016) Digital twin data modeling with automationML and a communication methodology for data exchange. IFAC-PapersOnLine 49(30):12–17

    Article  Google Scholar 

  3. Zhang H, Liu Q, Chen X et al (2017) A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 2017(5):26901–26911

    Article  Google Scholar 

  4. Ma Z, Sun G, Liu D et al (2016) Dissipativity analysis for discrete-time fuzzy neural networks with leakage and time-varying delays. Neurocomputing 175(JAN.29PT.A):579–584

    Article  Google Scholar 

  5. Yucel E, Ali MS, Gunasekaran N et al (2016) Sampled-data filtering of Takagi-Sugeno fuzzy neural networks with interval time-varying delays. Fuzzy Sets Syst 316(JUN.1):69–81

    MathSciNet  MATH  Google Scholar 

  6. Howe MS (2018) Pressure transients generated when high-speed trains pass in a tunnel. IMA J Appl Math 65(3):315–334

    Article  MathSciNet  MATH  Google Scholar 

  7. Park H, Kim J (2016) Electromagnetic induction energy harvester for high-speed railroad applications. Int J Precis Eng Manuf Green Technol 3(1):41–48

    Article  Google Scholar 

  8. Li BR, Wang Y, Dai GH et al (2019) Framework and case study of cognitive maintenance in Industry 4.0. Front Inf Technol Electron Eng 20(11):1493–1504

    Article  Google Scholar 

  9. Li L, Wang Y, Lin KY (2021) Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization. J Intell Manuf 32(2):545–558

    Article  Google Scholar 

  10. Pham BT, Bui DT et al (2016) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Nat Hazards 83(1):97–127

    Article  Google Scholar 

  11. Rubaai A, Young P (2016) Hardware/software implementation of fuzzy-neural-network self-learning control methods for brushless DC motor drives. IEEE Trans Ind Appl 52(1):414–424

    Article  Google Scholar 

  12. Acikgoz H, Coteli R, Ustundag M et al (2018) Robust control of current controlled pwm rectifiers using type-2 fuzzy neural networks for unity power factor operation. J Electr Eng Technol 13(2):822–828

    Google Scholar 

  13. Ebrahimi A, Fritsching U, Heuser M et al (2020) A digital twin approach to predict and compensate distortion in a high pressure die casting (HPDC) process chain. Procedia Manuf 52(7):144–149

    Article  Google Scholar 

  14. Jafari MA, Zaidan E, Ghofrani A et al (2020) Improving building energy footprint and asset performance using digital twin technology. IFAC-PapersOnLine 53(3):386–391

    Article  Google Scholar 

  15. Xu K, Zeng J, Wei L (2019) An analysis of the self-excited torsional vibration of high-speed train drive system. J Mech Sci Technol 33(3):1149–1158

    Article  Google Scholar 

  16. Kim JS, Park NG, Lee HW (2016) Vibration analysis of a planetary gear system based on the transfer matrix method. J Mech Sci Technol 30(2):611–621

    Article  Google Scholar 

  17. Dong MG, Liu B, Jing C (2020) A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization. Front Inf Technol Electron Eng 21(8):1171–1190

    Article  Google Scholar 

  18. Bill P (2016) Speed up damage assessment: assembly and dispatch of resources during outage events. Electr Today 29(4):8–10

    MathSciNet  Google Scholar 

  19. Zhuang H, Li W, Yin Y et al (2018) Collaborative analysis of dispatch systems between high-speed and conventional rail. Xinan Jiaotong Daxue Xuebao/J Southwest Jiaotong Univ 53(3):467–473

    Google Scholar 

  20. Hembroff CC, Arbuthnott KD, Kratzig GP (2018) Emergency response driver training: dual-task decrements of dispatch communication. Transp Res Part F Traffic Psychol Behav 59(PT.A):222–235

    Article  Google Scholar 

  21. Tan L, Wang H, Yang C et al (2017) A multi-objective optimization method based on discrete bacterial algorithm for environmental/economic power dispatch. Nat Comput 16(4):549–565

    Article  MathSciNet  Google Scholar 

  22. Zhang Y, Qin N, Huang D et al (2019) Fault diagnosis of high-speed train bogie based on deep neural network. IFAC-PapersOnLine 52(24):135–139

    Article  Google Scholar 

  23. Alvarez-Sanjaime O, Cantos-Sanchez P, Moner-Colonques R et al (2016) Rail access charges and internal competition in high speed trains. Transp Policy 49(jul.):184–195

    Article  Google Scholar 

  24. Wang C, Mao P, Mao Y et al (2016) Research on scheduling and optimization under uncertain conditions in panel block production line in shipbuilding. Int J Nav Archit Ocean Eng 8(4):398–408

    Article  Google Scholar 

  25. Liu LL, Wan X, Gao Z et al (2019) Research on modelling and optimization of hot rolling scheduling. J Ambient Intell Humaniz Comput 10(3):1201–1216

    Article  Google Scholar 

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Acknowledgements

This work was undertaken with the support of Science and Technology Nova Plan of Beijing City Fengtai District 2020-kjxx202006, and Beijing Nova Program (Z211100002121140).

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Authors and Affiliations

  1. Department of Industrial Engineering, Tsinghua University, Haidian, Beijing, 100084, China

    Fei Peng & Li Zheng

  2. CRRC Academy Company Limited, Fengtai, Beijing, 100070, China

    Fei Peng

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  1. Fei Peng
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  2. Li Zheng
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Correspondence to Fei Peng.

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Peng, F., Zheng, L. Fuzzy rule-based neural network for high-speed train manufacturing system scheduling problem. Neural Comput & Applic 35, 2077–2088 (2023). https://doi.org/10.1007/s00521-022-07190-9

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  • DOI: https://doi.org/10.1007/s00521-022-07190-9

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