Volume 21 Issue 6
Dec.  2021
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(6): 259-268
SONG Ying, LIANG Lei, WANG Yue, SHI Wen-jie. Wheel polygon state recognition method based on improved EEMD-WVD joint time-frequency analysis[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 259-268. doi: 10.19818/j.cnki.1671-1637.2021.06.020
Citation: SONG Ying, LIANG Lei, WANG Yue, SHI Wen-jie. Wheel polygon state recognition method based on improved EEMD-WVD joint time-frequency analysis[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 259-268. doi: 10.19818/j.cnki.1671-1637.2021.06.020
shu

Wheel polygon state recognition method based on improved EEMD-WVD joint time-frequency analysis

doi: 10.19818/j.cnki.1671-1637.2021.06.020
  • 1.

    School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China

  • 2.

    School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

  • 3.

    Infrastructure Inspection Research Institute, China Academy of Railway Sciences Co., Ltd., Beijing 100081, China

Funds:

National Natural Science Foundation of China 12072207

Natural Science Foundation of Hebei Province E2019210152

China Postdoctoral Science Foundation 2018M643521

Science and Technology Research Project of Higher Education of Hebei Province QN2018108

More Information
  • Author Bio:

    SONG Ying(1981-), female, associate professor, PhD, songy@stdu.edu.cn

  • Received Date: 2021-07-10
    Available Online: 2022-02-11
  • Publish Date: 2021-12-01
    Abstract
  • To accurately identify wheel polygon state and wheel abrasion amplitude of high-speed EMUs, a random vibration signal's joint time-frequency analysis method combing improved ensemble empirical mode decomposition (EEMD) and Wigner-Ville distribution (WVD) was presented. The correlation coefficient method and spectrum analysis were used to evaluate and filter the EEMD decomposition variables of the axle box vibration acceleration signal. Subsequently, the WVD of each intrinsic mode component was calculated for maintaining a high time-frequency resolution and effectively curbing cross-interference items. The method was applied to analyze the vibration acceleration signals of axle box caused by periodic and measured random wheel polygons. Research results indicate that the wheel polygon type can be recognized using the dominant frequency of the two-dimensional time-frequency spectrum of EEMD-WVD, and the abrasion amplitude can be evaluated using the energy amplitude distribution of the three-dimensional time-frequency-energy spectrum of EEMD-WVD, with the maximum error of 0.3%. Compared with the time-frequency analytical results by the short-time Fourier transform, wavelet transforms, and WVD method, the improved EEMD-WVD joint time-frequency analysis method dose not require parametric variation, has strong adaptability, retains the characteristics of WVD high time-frequency resolution, and effectively curbs both the modal aliasing phenomenon caused by EEMD decomposition and the cross-interference items caused by WVD distribution. The current study verifies the effectiveness and advantages of the proposed joint time-frequency analysis method, providing a novel technical approach for wheel polygon recognition and the evaluation of high-speed EMUs. 1 tab, 11 figs, 30 refs.

     

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