|Table of Contents|

[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, (1): 33-41. [doi:10.3969/j.issn.1003-7985.2021.01.005]
Copy

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

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

Volumn:
Issue:
2021年第1期
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:

[1] Zhao D, Liu S L, Gu D, et al. Improved multi-scale entropy and its application in rolling bearing fault feature extraction[J]. Measurement, 2020, 152:107361. DOI:10.1016/j.measurement.2019.107361.
[2] Cui L L, Wang X, Xu Y G, et al. A novel switching unscented Kalman filter method for remaining useful life prediction of rolling bearing [J]. Measurement, 2019, 135: 678-684. DOI:10.1016/j.measurement.2018.12.028.
[3] Glowacz A, Glowacz W, Glowacz Z, et al. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals[J].Measurement, 2018, 113: 1-9. DOI:10.1016/j.measurement.2017.08.036.
[4] Su W S, Wang F T, Zhu H, et al. Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement[J]. Mechanical Systems and Signal Processing, 2010, 24(5): 1458-1472. DOI:10.1016/j.ymssp.2009.11.011.
[5] Liu J, Xu Z D, Zhou L, et al. A statistical feature investigation of the spalling propagation assessment for a ball bearing[J]. Mechanism and Machine Theory, 2019, 131: 336-350. DOI:10.1016/j.mechmachtheory.2018.10.007.
[6] Chen B J, Shen B M, Chen F F, et al. Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing[J]. Measurement, 2019, 131: 400-411. DOI:10.1016/j.measurement.2018.07.043.
[7] Ding P, Jia M P, Wang H. A dynamic structure-adaptive symbolic approach for slewing bearings’ life prediction under variable working conditions[J]. Structural Health Monitoring, 2020:147592172092993. DOI:10.1177/1475921720929939.
[8] Jiang R L, Chen J, Dong G M, et al. The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2013, 227(5): 1116-1129. DOI:10.1177/0954406212457892.
[9] Sawalhi N, Randall R B. Signal pre-whitening using cepstrum editing(liftering)to enhance fault detection in rolling element bearings[C]// Proceedings of the 24 International Congress on Condition Monitoring and Diagnostic Engineering Management. Kolkata, India, 2011: 330-336.
[10] Zhang X, Hu Y, Hu L, et al. Enhanced detection of bearing faults based on signal cepstrum pre-whitening and stochastic resonance[J]. Journal of Mechanical Engineering, 2012, 48(23):83-89. DOI:10.3901/JME.2012.23.083. (in Chinese)
[11] Pan H Y, Yang Y, Li X, et al. Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis[J]. Mechanical Systems and Signal Processing, 2019, 114: 189-211. DOI:10.1016/j.ymssp.2018.05.019.
[12] Selesnick I W. Wavelet transform with tunable Q-factor[J]. IEEE Transactions on Signal Processing, 2011, 59(8): 3560-3575. DOI:10.1109/tsp.2011.2143711.
[13] Wu Z H, Huang N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41. DOI:10.1142/s1793536909000047.
[14] Cheng J. Local characteristic-scale decomposition method and its application to gear fault diagnosis[J]. Journal of Mechanical Engineering, 2012, 48(09):64-71. DOI:10.3901/JME.2012.09.064. (in Chinese)
[15] Yi C C, Lü Y, Dang Z, et al. Quaternion singular spectrum analysis using convex optimization and its application to fault diagnosis of rolling bearing[J]. Measurement, 2017, 103: 321-332. DOI:10.1016/j.measurement.2017.02.047.
[16] Yan X, Jia M, Morphological demodulation method based on improved singular spectrum decomposition and its application in rolling bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2017, 53(7): 104-112. DOI:10.3901/JME.2017.07.104. (in Chinese)
[17] Zhao X, Ye B, Chen T. Extraction method of faint fault feature based on wavelet-SVD difference spectrum[J]. Journal of Mechanical Engineering, 2012, 48(7):37-48. DOI:10.3901/JME.2012.07.037. (in Chinese)

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.
Last Update: 2021-03-20