Skip to main content
SpringerLink
Log in

Numerical research on virtual reality of vibration characteristics of the motor based on GA-BPNN model

  • Neural Computing in Next Generation Virtual Reality Technology
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper firstly established the finite element model of steel shell motor, computed modal frequencies on top 6 orders to compare with experimental results and verified the reliability of the finite element model. Then, this paper numerically calculated the electromagnetic force of the motor, inputted it into the verified finite element model and computed the vibration acceleration, velocity, stress and strain of the motor. Constraint and properties of internal materials remained unchanged. Steel shell was replaced by aluminum alloy shell to recompute the vibration acceleration, velocity, stress and strain of the motor and compare with those of steel structure motor. Results showed that the motor of aluminum alloy shell had more obvious vibration characteristics. Finally, this paper put forward neural network model optimized by GA. This model was used to predict the vibration characteristics of the motor of aluminum alloy shell and compare with the real value calculated by finite element, showing good consistency. It indicated that it was feasible to predict the vibration characteristics of the motor based on GA-BPNN model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Sun T, Kim JM, Lee GH et al (2011) Effect of pole and slot combination on noise and vibration in permanent magnet synchronous motor. IEEE Trans Magn 47(5):1038–1041

    Article  Google Scholar 

  2. Shin PS, Cheung HJ (2011) A magnetostrictive force and vibration mode analysis of 3 kW BLDC motor by a magneto-mechanical coupling formulation. J Electr Eng Technol 6(1):76–80

    Article  Google Scholar 

  3. Pollock RD, Woledge RC, Martin FC et al (2012) Effects of whole body vibration on motor unit recruitment and threshold. J Appl Physiol 112(3):388–395

    Article  Google Scholar 

  4. McBride JM, Nuzzo JL, Dayne AM et al (2010) Effect of an acute bout of whole body vibration exercise on muscle force output and motor neuron excitability. J Strength Cond Res 24(1):184–189

    Article  Google Scholar 

  5. Qiu JJ (2002) Investigation on coupled mechanical and electrical vibration and coupled magnetical and solid vibration of electrical machine. Proc Chin Soc Electr Eng 5(5):109–115

    Google Scholar 

  6. Qiu JJ (1981) Study of stability of motion shock absorption and parameters vibration of rotor of steam turbine generator excited by electromagnetic forces. J Tianjin Univ 4:83–96

    Google Scholar 

  7. Wu HM, Jia QF (2011) Research on nonlinear vibration of the motor rigid body model. J Dyn Control 9(3):222–226

    Google Scholar 

  8. Ma CG, Zuo SG, Tan QW et al (2013) Nonlinear torsional vibration model of a PMSM for electric driven vehicle. J Vib Shock 32(12):131–134

    Google Scholar 

  9. Tomczuk B, Sobo M (2005) A field-network model of a linear oscillating motor and its dynamics characteristics. Magnetics 8:2362–2367

    Article  Google Scholar 

  10. Lv Z, Chen G, Zhong C et al (2012) A framework for multi-dimensional webgis based interactive online virtual community. Adv Sci Lett 7(1):215–219

    Article  Google Scholar 

  11. Lv Z, Halawani A, Feng S et al (2015) Touch-less interactive augmented reality game on vision-based wearable device. Pers Ubiquitous Comput 19(3–4):551–567

    Article  Google Scholar 

  12. Guo Z, Wu J, Lu H et al (2011) A case study on a hybrid wind speed forecasting method using BP neural network. Knowl Based Syst 24(7):1048–1056

    Article  Google Scholar 

  13. Ding S, Su C, Yu J (2011) An optimizing BP neural network algorithm based on genetic algorithm. Artif Intell Rev 36(2):153–162

    Article  Google Scholar 

  14. Kourehli SS (2015) Damage quantification method using artificial neural network and static response with limited sensors. J Vibroeng 17(3):1317–1325

    Google Scholar 

  15. Qian K, Liang J, Gao Y (2015) The prediction of vibration and noise for the high-speed train based on neural network and boundary element method. J Vibroeng 17(8):4445–4457

    Google Scholar 

  16. Shuran L, Shujin L (2011) Applying BP neural network model to forecast peak velocity of blasting ground vibration. Proced Eng 26:257–263

    Article  Google Scholar 

  17. Wei Wei, Yong Qi (2011) Information potential fields navigation in wireless Ad-Hoc sensor networks. Sensors 11(5):4794–4807

    Article  MathSciNet  Google Scholar 

  18. Chen Z, Li C, Sánchez RV (2015) Multi-layer neural network with deep belief network for gearbox fault diagnosis. J Vibroeng 17(5):2379–2392

    Google Scholar 

  19. Hui KH, Ooi CS, Lim MH et al (2016) A hybrid artificial neural network with Dempster-Shafer theory for automated bearing fault diagnosis. J Vibroeng 18(7):4409–4418

    Article  Google Scholar 

  20. Ke L, Wenyan G, Xiaoliu S et al (2012) Research on the forecast model of electricity power industry loan based on GA-BP neural network. Energy Proced 14:1918–1924

    Article  Google Scholar 

  21. Wang Y, Wang S, Zhang N (2013) A novel wind speed forecasting method based on ensemble empirical mode decomposition and GA-BP neural network. In: Power and energy society general meeting (PES), 2013 IEEE. IEEE, pp 1–5

  22. Xie S, Zhou H, Zhao J et al (2013) Energy-absorption forecast of thin-walled structure by GA-BP hybrid algorithm. J Cent South Univ 20(4):1122–1128

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Henan Institute of Technology, Xinxiang, China

    Xin-ya Chen

  2. Department of Electronic and Communication Engineering, Henan Institute of Technology, Xinxiang, China

    Zhen Chen

  3. Department of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan, China

    Yang Zhao

Authors
  1. Xin-ya Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhen Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Xy., Chen, Z. & Zhao, Y. Numerical research on virtual reality of vibration characteristics of the motor based on GA-BPNN model. Neural Comput & Applic 29, 1343–1355 (2018). https://doi.org/10.1007/s00521-017-2923-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-2923-x

Keywords

Search

Navigation