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Underdetermined blind source extraction of early vehicle bearing faults based on EMD and kernelized correlation maximization

Author

Listed:
  • Xuejun Zhao

    (Beijing Jiaotong University)

  • Yong Qin

    (Beijing Jiaotong University
    Beijing Jiaotong University
    National Engineering Laboratory for System Safety and Operation Assurance of Urban Rail Transit)

  • Changbo He

    (Anhui University)

  • Limin Jia

    (Beijing Jiaotong University
    Beijing Jiaotong University
    National Engineering Laboratory for System Safety and Operation Assurance of Urban Rail Transit)

Abstract

The incipient bearing fault diagnosis is crucial to the industrial machinery maintenance. Developed based on the blind source separation, blind source extraction (BSE) has recently become the focus of intensive research work. However, owing to certain industrial restrictions, the number of sensors is usually less than that of the source signals, which is defined as an underdetermined BSE problem to identify the fault signals. The kernelized methods are found to be robust to the noise, especially in the presence of outliers, which makes it a suitable tool to extract fault signatures submerged in the strong environment noise. Thus, this paper proposes a new underdetermined BSE method based on the empirical mean decomposition and kernelized correlation. The experimental results indicate that the extracted fault signature presents more obvious periodicity. Two important parameters of this method, including the multi-shift number and the kernel size are investigated to improve the algorithm performance. Furthermore, performance comparisons with underdetermined BSE based on the second order correlation are made to emphasize the advantage of the presented method. The application of the proposed method is validated using the simulated signal and the rolling element bearing signal of the train vehicle axle.

Suggested Citation

  • Xuejun Zhao & Yong Qin & Changbo He & Limin Jia, 2022. "Underdetermined blind source extraction of early vehicle bearing faults based on EMD and kernelized correlation maximization," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 185-201, January.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:1:d:10.1007_s10845-020-01655-1
    DOI: 10.1007/s10845-020-01655-1
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    References listed on IDEAS

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    1. Cosmena Mahapatra & Ashish Payal & Meenu Chopra, 2020. "Swarm intelligence based centralized clustering: a novel solution," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1877-1888, December.
    2. Pradeep Kundu & Seema Chopra & Bhupesh K. Lad, 2019. "Multiple failure behaviors identification and remaining useful life prediction of ball bearings," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1795-1807, April.
    3. Wo Jae Lee & Gamini P. Mendis & Matthew J. Triebe & John W. Sutherland, 2020. "Monitoring of a machining process using kernel principal component analysis and kernel density estimation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1175-1189, June.
    4. Cong Wang & Meng Gan & Chang’an Zhu, 2018. "Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 937-951, April.
    5. Huaqing Wang & Ruitong Li & Gang Tang & Hongfang Yuan & Qingliang Zhao & Xi Cao, 2014. "A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-13, October.
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