Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy

Dongkai Li; Xiaoang Liu; Yue You; Dong Zhen; Wei Hu; Kuihua Lu; Fengshou Gu

doi:10.1007/978-3-030-99075-6_52

Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy

Dongkai Li, Xiaoang Liu, Yue You, Dong Zhen, Wei Hu, Kuihua Lu, Fengshou Gu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

As the main parts of rotating machinery, rolling bearing is prone to failure due to its harsh working environment. Aiming at the problem that the early fault features of a rolling bearing are easily submerged by noise and difficult to extract, a fault diagnosis method based on weighted variational mode decomposition (WVMD) and cyclic spectrum slice energy (CSSE) is proposed. Firstly, the signal is decomposed into intrinsic mode functions (IMFs) by VMD and the sparsity is used to measure the amount of information contained in each IMF, and all IMFs are weighted and reconstructed to suppress the noise interference components in the signal. Secondly, the advantage of the CSSE which can accurately mediate the fault information is used to analyze the reconstructed signal, and then the fault characteristic frequency of the reconstructed signal is extracted. Finally, the bearing simulation signal and outer ring fault signal are used to verify that the proposed diagnosis method can effectively extract the early fault features of rolling bearing.

Original language	English
Title of host publication	Proceedings of IncoME-VI and TEPEN 2021
Subtitle of host publication	Performance Engineering and Maintenance Engineering
Editors	Hao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
Publisher	Springer, Cham
Pages	643-654
Number of pages	12
Volume	117
ISBN (Electronic)	9783030990756
ISBN (Print)	9783030990749
DOIs	https://doi.org/10.1007/978-3-030-99075-6_52
Publication status	Published - 18 Sep 2022
Event	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 - Hebei University of Technology, Tianjin, China Duration: 20 Oct 2021 → 23 Oct 2021 Conference number: 6 https://tepen.net/conference/tepen2021/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	117
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
Abbreviated title	TEPEN-2021 and IncoME-VI
Country/Territory	China
City	Tianjin
Period	20/10/21 → 23/10/21
Internet address	https://tepen.net/conference/tepen2021/

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10.1007/978-3-030-99075-6_52Licence: Unspecified

Cite this

Li, D., Liu, X., You, Y., Zhen, D., Hu, W., Lu, K., & Gu, F. (2022). Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy. In H. Zhang, G. Feng, H. Wang, F. Gu, & J. K. Sinha (Eds.), Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering (Vol. 117, pp. 643-654). (Mechanisms and Machine Science; Vol. 117). Springer, Cham. https://doi.org/10.1007/978-3-030-99075-6_52

Li, Dongkai ; Liu, Xiaoang ; You, Yue et al. / Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy. Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. editor / Hao Zhang ; Guojin Feng ; Hongjun Wang ; Fengshou Gu ; Jyoti K. Sinha. Vol. 117 Springer, Cham, 2022. pp. 643-654 (Mechanisms and Machine Science).

@inproceedings{51e4057ec8074a00a2b1abcacc7ac5d2,

title = "Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy",

abstract = "As the main parts of rotating machinery, rolling bearing is prone to failure due to its harsh working environment. Aiming at the problem that the early fault features of a rolling bearing are easily submerged by noise and difficult to extract, a fault diagnosis method based on weighted variational mode decomposition (WVMD) and cyclic spectrum slice energy (CSSE) is proposed. Firstly, the signal is decomposed into intrinsic mode functions (IMFs) by VMD and the sparsity is used to measure the amount of information contained in each IMF, and all IMFs are weighted and reconstructed to suppress the noise interference components in the signal. Secondly, the advantage of the CSSE which can accurately mediate the fault information is used to analyze the reconstructed signal, and then the fault characteristic frequency of the reconstructed signal is extracted. Finally, the bearing simulation signal and outer ring fault signal are used to verify that the proposed diagnosis method can effectively extract the early fault features of rolling bearing.",

keywords = "Cyclic spectrum slice energy, Fault diagnosis, Rolling bearing, Variational mode decomposition, Weight coefficient",

author = "Dongkai Li and Xiaoang Liu and Yue You and Dong Zhen and Wei Hu and Kuihua Lu and Fengshou Gu",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, TEPEN-2021 and IncoME-VI ; Conference date: 20-10-2021 Through 23-10-2021",

year = "2022",

month = sep,

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doi = "10.1007/978-3-030-99075-6_52",

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volume = "117",

series = "Mechanisms and Machine Science",

publisher = "Springer, Cham",

pages = "643--654",

editor = "Hao Zhang and Guojin Feng and Hongjun Wang and Fengshou Gu and Sinha, {Jyoti K.}",

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Li, D, Liu, X, You, Y, Zhen, D, Hu, W, Lu, K & Gu, F 2022, Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy. in H Zhang, G Feng, H Wang, F Gu & JK Sinha (eds), Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. vol. 117, Mechanisms and Machine Science, vol. 117, Springer, Cham, pp. 643-654, 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, Tianjin, China, 20/10/21. https://doi.org/10.1007/978-3-030-99075-6_52

Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy. / Li, Dongkai; Liu, Xiaoang; You, Yue et al.

Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. ed. / Hao Zhang; Guojin Feng; Hongjun Wang; Fengshou Gu; Jyoti K. Sinha. Vol. 117 Springer, Cham, 2022. p. 643-654 (Mechanisms and Machine Science; Vol. 117).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy

AU - Li, Dongkai

AU - Liu, Xiaoang

AU - You, Yue

AU - Zhen, Dong

AU - Hu, Wei

AU - Lu, Kuihua

AU - Gu, Fengshou

N1 - Conference code: 6

PY - 2022/9/18

Y1 - 2022/9/18

N2 - As the main parts of rotating machinery, rolling bearing is prone to failure due to its harsh working environment. Aiming at the problem that the early fault features of a rolling bearing are easily submerged by noise and difficult to extract, a fault diagnosis method based on weighted variational mode decomposition (WVMD) and cyclic spectrum slice energy (CSSE) is proposed. Firstly, the signal is decomposed into intrinsic mode functions (IMFs) by VMD and the sparsity is used to measure the amount of information contained in each IMF, and all IMFs are weighted and reconstructed to suppress the noise interference components in the signal. Secondly, the advantage of the CSSE which can accurately mediate the fault information is used to analyze the reconstructed signal, and then the fault characteristic frequency of the reconstructed signal is extracted. Finally, the bearing simulation signal and outer ring fault signal are used to verify that the proposed diagnosis method can effectively extract the early fault features of rolling bearing.

AB - As the main parts of rotating machinery, rolling bearing is prone to failure due to its harsh working environment. Aiming at the problem that the early fault features of a rolling bearing are easily submerged by noise and difficult to extract, a fault diagnosis method based on weighted variational mode decomposition (WVMD) and cyclic spectrum slice energy (CSSE) is proposed. Firstly, the signal is decomposed into intrinsic mode functions (IMFs) by VMD and the sparsity is used to measure the amount of information contained in each IMF, and all IMFs are weighted and reconstructed to suppress the noise interference components in the signal. Secondly, the advantage of the CSSE which can accurately mediate the fault information is used to analyze the reconstructed signal, and then the fault characteristic frequency of the reconstructed signal is extracted. Finally, the bearing simulation signal and outer ring fault signal are used to verify that the proposed diagnosis method can effectively extract the early fault features of rolling bearing.

KW - Cyclic spectrum slice energy

KW - Fault diagnosis

KW - Rolling bearing

KW - Variational mode decomposition

KW - Weight coefficient

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UR - https://link.springer.com/book/10.1007/978-3-030-99075-6

U2 - 10.1007/978-3-030-99075-6_52

DO - 10.1007/978-3-030-99075-6_52

M3 - Conference contribution

AN - SCOPUS:85138781420

SN - 9783030990749

VL - 117

T3 - Mechanisms and Machine Science

SP - 643

EP - 654

BT - Proceedings of IncoME-VI and TEPEN 2021

A2 - Zhang, Hao

A2 - Feng, Guojin

A2 - Wang, Hongjun

A2 - Gu, Fengshou

A2 - Sinha, Jyoti K.

PB - Springer, Cham

T2 - 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021

Y2 - 20 October 2021 through 23 October 2021

ER -

Li D, Liu X, You Y, Zhen D, Hu W, Lu K et al. Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy. In Zhang H, Feng G, Wang H, Gu F, Sinha JK, editors, Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. Vol. 117. Springer, Cham. 2022. p. 643-654. (Mechanisms and Machine Science). Epub 2022 Sep 18. doi: 10.1007/978-3-030-99075-6_52

Rolling Bearing Fault Diagnosis Based on Weighted Variational Mode Decomposition and Cyclic Spectrum Slice Energy

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