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Research on the virtual reality of vibration characteristics in vehicle cabin based on neural networks

  • Neural Computing in Next Generation Virtual Reality Technology
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Abstract

A finite element model of commercial vehicles was firstly built in the paper to study the virtual reality of vibration characteristics, and the top 6-order modal was then computed and compared with the experimental results to verify the reliability of the computational model. Then, a neural network model of the cabin was built. Through road tests, the excitation signal at the cabin suspension point and the response signal of interior vibration noise were measured under the idle condition and constant speed condition. The measured excitation signal was applied to the prediction model for frequency response analysis, in order to compute the interior noise within the range 20–200 Hz. The obtained simulation result of the vibration noise was compared with the experimental result and analyzed. As indicated from the analysis, the influence of excitation spectrum and the model can be reflected by the simulation response spectrum, which is consistent with the experimental result. The higher precision can be also obtained when the model is applied to predict the interior noise.

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References

  1. Andrzej P, Tage B (2007) An investigation of the coupling between the passenger compartment and the truck in a sedan. SAE 2007:2356–2362

    Google Scholar 

  2. Norimasa K, Masami H, Hiroo Y (2009) Development for dynamic response analysis of acoustic trim. SAE 2009:1487–1493

    Google Scholar 

  3. Liu Y, Yu F, Liu J (2005) Modal analysis of vehicle compartment with acoustic-structure coupling. Noise Vib Control 5:38–40

    Google Scholar 

  4. Laurent G, Morad K, Energy D (2009) Field approach for low and medium frequency vibration acoustic analysis of a car body using a probabilistic computational model. SAE 2009:849–863

    Google Scholar 

  5. Kruntcheva MR (2007) Acoustic-structural coupling of the automobile passenger compartment. In: World congress on engineering, pp 1236–1241

  6. Coroian A, Lupea I (2013) Improving the sound pressure level for a simplified passenger cabin by using modal participation and size optimization. Rom J Acoust Vib 10(1):100–106

    Google Scholar 

  7. Yansong W, Xintian L, Yan L (2010) A vibro-acoustic coupling analysis of bus passenger compartment based on the finite element method. In: International conference on intelligent computation technology and automation (ICICTA), 2010, vol 1. IEEE, pp 386–389

  8. Zhang Y, Wu L (2009) Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Syst Appl 36(5):8849–8854

    Article  Google Scholar 

  9. Zhang L, Wu K, Zhong Y et al (2008) A new sub-pixel mapping algorithm based on a BP neural network with an observation model. Neurocomputing 71(10):2046–2054

    Article  Google Scholar 

  10. Ruan JH, Shi Y (2016) Monitoring and assessing fruit freshness in IOT-based e-commerce delivery using scenario analysis and interval number approaches. Inf Sci 373(10):557–570

    Article  Google Scholar 

  11. 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 

  12. Fuh KH, Wang SB (1997) Force modeling and forecasting in creep feed grinding using improved BP neural network. Int J Mach Tools Manuf 37(8):1167–1178

    Article  Google Scholar 

  13. Mehrara H, Zahedinejad M, Pourmohammad A (2009) Novel edge detection using BP neural network based on threshold binarization. In: Second international conference on computer and electrical engineering, 2009. ICCEE’09, vol 2. IEEE, pp 408–412

  14. Pendharkar PC (2005) A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Comput Oper Res 32(10):2561–2582

    Article  MATH  Google Scholar 

  15. Wei W, Fan X, Song H et al (2016) Imperfect information dynamic Stackelberg game based resource allocation using hidden markov for cloud computing. IEEE Trans Serv Comput 99:1–10

    Google Scholar 

  16. Li JQ, He SQ, Ming Z (2015) An intelligent wireless sensor networks system with multiple servers communication. Int J Distrib Sensor Netw 2015(6):1–9

    Google Scholar 

  17. Ruan JH, Wang XP (2016) Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering. Int J Prod Res 54(14):4368–4386

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China (61503049), China Postdoctoral Science Foundation Funded Project (2016T90838; 2015M582525), Specialized Research Fund for the Doctoral Program of Higher Education of China (20135522110003) and Open Fund of Chongqing Key Laboratory of Traffic & Transportation (2016CQJY004).

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Authors and Affiliations

  1. School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, 400074, China

    Jinshuan Peng, Lei Xu & Yiming Shao

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  1. Jinshuan Peng
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  2. Lei Xu
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  3. Yiming Shao
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Correspondence to Jinshuan Peng.

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Peng, J., Xu, L. & Shao, Y. Research on the virtual reality of vibration characteristics in vehicle cabin based on neural networks. Neural Comput & Applic 29, 1225–1232 (2018). https://doi.org/10.1007/s00521-016-2829-z

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  • DOI: https://doi.org/10.1007/s00521-016-2829-z

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