Volume 21 Issue 6
Dec.  2021
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NIU Yi-jie, LI Hua, DENG Wu, FEI Ji-you, SUN Ya-li, LIU Zhi-bo. Rolling bearing fault diagnosis method based on TQWT and sparse representation[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 237-246. doi: 10.19818/j.cnki.1671-1637.2021.06.018
Citation: NIU Yi-jie, LI Hua, DENG Wu, FEI Ji-you, SUN Ya-li, LIU Zhi-bo. Rolling bearing fault diagnosis method based on TQWT and sparse representation[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 237-246. doi: 10.19818/j.cnki.1671-1637.2021.06.018
shu

Rolling bearing fault diagnosis method based on TQWT and sparse representation

doi: 10.19818/j.cnki.1671-1637.2021.06.018
  • 1.

    Software Technology Institute, Dalian Jiaotong University, Dalian 116028, Liaoning, China

  • 2.

    College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, Liaoning, China

  • 3.

    College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

  • 4.

    School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, Liaoning, China

Funds:

National Natural Science Foundation of China 62001079

National Natural Science Foundation of China 51605068

National Key Technology Research and Development Program 2015BAF20B02

Scientific Research Funds of Education Department of Liaoning Province LJKZ0481

More Information
  • Author Bio:

    NIU Yi-jie(1978-), female, assistant professor, doctoral student, 84848217@qq.com

    FEI Ji-you(1964-), male, professor, PhD, fjy@djtu.edu.cn

  • Corresponding author: DENG Wu (1976-), male, professor, PhD, wdeng@cauc.edu.cn
  • Received Date: 2021-06-18
    Available Online: 2022-02-11
  • Publish Date: 2021-12-01
    Abstract
  • Based on the sparse representation theory, a new method of rolling bearing fault diagnosis was proposed using the tunable-Q wavelet transform (TQWT). The characteristics of the original vibration signals and early fault signals containing early fault components were analyzed, and the applications of the sparse representation model to solve the problem of fault feature extraction and fault type recognition were studied. The original signal was transformed into a set of sub-band wavelet coefficients using the TQWT. The effectiveness of extracting sparse wavelet coefficients using an iterative threshold shrinkage algorithm and the sensitivity of spectral kurtosis to fault impact signals were studied. By calculating the spectral kurtosis of each sub-band signal component and selecting the sub-band wavelet coefficient that contains obvious fault information, a fault feature extraction method for the sparse fault signal component was established. Using the sparse representation classification model of extracted fault signals, the method of rolling bearing fault-type recognition based on sparse representation was realized. Experimental results indicate that the proposed fault feature extraction method has a significant effect in eliminating interference components in the Case Western Reserve University dataset. The average diagnostic accuracy for the four types of data is 99.83%. The average diagnostic accuracy for the 10 types of data is 97.73%. Compared with the TQWT and iterative threshold shrinkage algorithm for fault feature extraction, the fault diagnosis accuracy of the proposed method improves by 11.60%, and the running time reduces by 8%. For the vibration dataset collected by the QPZZ-Ⅱ rotating machinery platform, the average diagnostic accuracy of the proposed method for the four types of data is 100%. Compared with the traditional wavelet denoising method, the accuracy of the proposed method improves by 35.67%, and the running time reduces by 7.25%. Therefore, the proposed method can effectively solve the problem of rolling-bearing fault diagnosis. 7 tabs, 7 figs, 30 refs.

     

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