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Author:

Cui, Lingli (Cui, Lingli.) (Scholars:崔玲丽) | Li, Beibei (Li, Beibei.) | Ma, Jianfeng (Ma, Jianfeng.) | Jin, Zhi (Jin, Zhi.)

Indexed by:

EI Scopus SCIE

Abstract:

Aiming at the misdiagnosis caused by extracting the non-fault features using Lempel-Ziv algorithm, a quantitative trend fault diagnosis method of a rolling bearing based on Sparsogram and Lempel-Ziv is proposed in this paper. The approximate linear relationships between Lempel-Ziv and the fault sizes of inner and outer races are simulated with a theoretical model of rolling bearings. Then, the fault characteristic of experimental signal of bearing inner and outer race in different severity is extracted by Sparsogram algorithm, and the frequency band is selected which reflects the fault characteristic mostly, and the Lempel-Ziv complexity index of the frequency band is calculated. This method can avoid the misdiagnosis caused by extracting non-fault features. Comparing with direct calculation of Lempel-Ziv value, the performance is much better on the quantitative diagnosis of a defective bearing. The experimental results show that the proposed method can realize the quantitative trend diagnosis on defective bearings.

Keyword:

Sparsogram Quantitative trend diagnosis Lempel-Ziv Rolling bearing

Author Community:

  • [ 1 ] [Cui, Lingli]Beijing Univ Technol, Key Lab Adv Mfg Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Li, Beibei]Beijing Univ Technol, Key Lab Adv Mfg Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Jin, Zhi]Beijing Univ Technol, Key Lab Adv Mfg Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Ma, Jianfeng]Beijing Univ Technol, Beijing Engn Res Ctr Precis Measurement Technol &, Beijing 100124, Peoples R China

Reprint Author's Address:

  • 崔玲丽

    [Cui, Lingli]Beijing Univ Technol, Key Lab Adv Mfg Technol, Beijing 100124, Peoples R China

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Related Keywords:

Source :

MEASUREMENT

ISSN: 0263-2241

Year: 2018

Volume: 128

Page: 410-418

5 . 6 0 0

JCR@2022

ESI Discipline: ENGINEERING;

ESI HC Threshold:156

Cited Count:

WoS CC Cited Count: 68

SCOPUS Cited Count: 81

ESI Highly Cited Papers on the List: 3 Unfold All

  • 2020-1
  • 2019-11
  • 2019-9

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

Online/Total:373/5461985
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