[1] Chen Yiya, Jia Minping, Yan Xiaoan,. A bearing fault feature extraction method based on cepstrumpre-whitening and a quantitative law of symplectic geometrymode decomposition [J]. Journal of Southeast University (English Edition), 2021, 37 (1): 33-41. [doi:10.3969/j.issn.1003-7985.2021.01.005]

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A bearing fault feature extraction method based on cepstrumpre-whitening and a quantitative law of symplectic geometrymode decomposition()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

37

Issue:

2021 1

Page:

33-41

Research Field:

Mechanical Engineering

Publishing date:

2021-03-20

Info

Title:

A bearing fault feature extraction method based on cepstrumpre-whitening and a quantitative law of symplectic geometrymode decomposition

Author(s):

Chen Yiya¹;  Jia Minping¹;  Yan Xiaoan²

¹ School of Mechanical Engineering, Southeast University, Nanjing 211189, China
² School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, China

Keywords:

cepstrum pre-whitening;  symplectic geometry mode decomposition;  eigenvalue;  quantitative law;  feature extraction

PACS:

TH17

DOI:

10.3969/j.issn.1003-7985.2021.01.005

Abstract:

In order to extract the fault feature of the bearing effectively and prevent the impact components caused by bearing damage being interfered with by discrete frequency components and background noise, a method of fault feature extraction based on cepstrum pre-whitening(CPW)and a quantitative law of symplectic geometry mode decomposition(SGMD)is proposed. First, CPW is performed on the original signal to enhance the impact feature of bearing fault and remove the periodic frequency components from complex vibration signals. The pre-whitening signal contains only background noise and non-stationary shock caused by damage. Secondly, a quantitative law that the number of effective eigenvalues of the Hamilton matrix is twice the number of frequency components in the signal during SGMD is found, and the quantitative law is verified by simulation and theoretical derivation. Finally, the trajectory matrix of the pre-whitening signal is constructed and SGMD is performed. According to the quantitative law, the corresponding feature vector is selected to reconstruct the signal. The Hilbert envelope spectrum analysis is performed to extract fault features. Simulation analysis and application examples prove that the proposed method can clearly extract the fault feature of bearings.

References:

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Memo

Memo:

Biographies: Chen Yiya(1995—), female, graduate; Jia Minping(corresponding author), male, doctor, professor, mpjia@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.52075095).
Citation: Chen Yiya, Jia Minping, Yan Xiaoan. A bearing fault feature extraction method based on cepstrum pre-whitening and a quantitative law of symplectic geometry mode decomposition.[J].Journal of Southeast University(English Edition), 2021, 37(1):33-41.DOI:10.3969/j.issn.1003-7985.2021.01.005.

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