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

Hui Liu; Zhiyu Shi

doi:doi:10.11648/j.ijmea.20200801.13

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

Hui Liu, Zhiyu Shi

Published in International Journal of Mechanical Engineering and Applications (Volume 8, Issue 1)

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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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

Hui Liu

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

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

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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}
}

Copy | Download

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  -

Copy | Download