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A Fault Detection Approach Using Variational Mode Decomposition, L-kurtosis and Random Decrement Technique for Rotating Machinery

Received: 6 January 2020     Accepted: 21 January 2020     Published: 31 January 2020
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Abstract

Fault detection of rotating machinery under heavy noise background, is a significant but difficult issue, and traditional fault detection approaches are difficult to apply. To address this problem, a novel approach that combines variational mode decomposition (VMD), L-Kurtosis and random decrement technique (RDT) is proposed, which procedures are summarized as follows. First, the raw vibration signal collected from the rotating component is decomposed using VMD into a set of intrinsic mode functions (IMFs), and the noise components can be separated from the raw signal. Second, the L-Kurtosis indicator is introduced to solve the problem that the fault information is difficult to track, and the optimal intrinsic mode function (IMF) can be determined according to the maximum L-Kurtosis value. Then, RDT is further employed to purify the optimal IMF to eliminate the other unknown interference sources. Finally, a Hilbert envelope spectrum analysis is used for detecting the fault type. In order to validate the proposed approach, the numerical simulations and real experimental investigations about rolling element bearing and gear are conducted. The results illustrate that the proposed approach can effectively detect faults of rotating components.

Published in International Journal of Mechanical Engineering and Applications (Volume 8, Issue 1)
DOI 10.11648/j.ijmea.20200801.13
Page(s) 16-26
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Rotating Machinery, Fault Detection, Variational Mode Decomposition, L-Kurtosis, Random Decrement Technique

References
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Cite This Article
  • APA Style

    Hui Liu, Zhiyu Shi. (2020). A Fault Detection Approach Using Variational Mode Decomposition, L-kurtosis and Random Decrement Technique for Rotating Machinery. International Journal of Mechanical Engineering and Applications, 8(1), 16-26. https://doi.org/10.11648/j.ijmea.20200801.13

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    ACS Style

    Hui Liu; Zhiyu Shi. A Fault Detection Approach Using Variational Mode Decomposition, L-kurtosis and Random Decrement Technique for Rotating Machinery. Int. J. Mech. Eng. Appl. 2020, 8(1), 16-26. doi: 10.11648/j.ijmea.20200801.13

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    AMA Style

    Hui Liu, Zhiyu Shi. A Fault Detection Approach Using Variational Mode Decomposition, L-kurtosis and Random Decrement Technique for Rotating Machinery. Int J Mech Eng Appl. 2020;8(1):16-26. doi: 10.11648/j.ijmea.20200801.13

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  • @article{10.11648/j.ijmea.20200801.13,
      author = {Hui Liu and Zhiyu Shi},
      title = {A Fault Detection Approach Using Variational Mode Decomposition, L-kurtosis and Random Decrement Technique for Rotating Machinery},
      journal = {International Journal of Mechanical Engineering and Applications},
      volume = {8},
      number = {1},
      pages = {16-26},
      doi = {10.11648/j.ijmea.20200801.13},
      url = {https://doi.org/10.11648/j.ijmea.20200801.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20200801.13},
      abstract = {Fault detection of rotating machinery under heavy noise background, is a significant but difficult issue, and traditional fault detection approaches are difficult to apply. To address this problem, a novel approach that combines variational mode decomposition (VMD), L-Kurtosis and random decrement technique (RDT) is proposed, which procedures are summarized as follows. First, the raw vibration signal collected from the rotating component is decomposed using VMD into a set of intrinsic mode functions (IMFs), and the noise components can be separated from the raw signal. Second, the L-Kurtosis indicator is introduced to solve the problem that the fault information is difficult to track, and the optimal intrinsic mode function (IMF) can be determined according to the maximum L-Kurtosis value. Then, RDT is further employed to purify the optimal IMF to eliminate the other unknown interference sources. Finally, a Hilbert envelope spectrum analysis is used for detecting the fault type. In order to validate the proposed approach, the numerical simulations and real experimental investigations about rolling element bearing and gear are conducted. The results illustrate that the proposed approach can effectively detect faults of rotating components.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - A Fault Detection Approach Using Variational Mode Decomposition, L-kurtosis and Random Decrement Technique for Rotating Machinery
    AU  - Hui Liu
    AU  - Zhiyu Shi
    Y1  - 2020/01/31
    PY  - 2020
    N1  - https://doi.org/10.11648/j.ijmea.20200801.13
    DO  - 10.11648/j.ijmea.20200801.13
    T2  - International Journal of Mechanical Engineering and Applications
    JF  - International Journal of Mechanical Engineering and Applications
    JO  - International Journal of Mechanical Engineering and Applications
    SP  - 16
    EP  - 26
    PB  - Science Publishing Group
    SN  - 2330-0248
    UR  - https://doi.org/10.11648/j.ijmea.20200801.13
    AB  - Fault detection of rotating machinery under heavy noise background, is a significant but difficult issue, and traditional fault detection approaches are difficult to apply. To address this problem, a novel approach that combines variational mode decomposition (VMD), L-Kurtosis and random decrement technique (RDT) is proposed, which procedures are summarized as follows. First, the raw vibration signal collected from the rotating component is decomposed using VMD into a set of intrinsic mode functions (IMFs), and the noise components can be separated from the raw signal. Second, the L-Kurtosis indicator is introduced to solve the problem that the fault information is difficult to track, and the optimal intrinsic mode function (IMF) can be determined according to the maximum L-Kurtosis value. Then, RDT is further employed to purify the optimal IMF to eliminate the other unknown interference sources. Finally, a Hilbert envelope spectrum analysis is used for detecting the fault type. In order to validate the proposed approach, the numerical simulations and real experimental investigations about rolling element bearing and gear are conducted. The results illustrate that the proposed approach can effectively detect faults of rotating components.
    VL  - 8
    IS  - 1
    ER  - 

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Author Information
  • State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China

  • State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China

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