Application of MED and GMCP Sparse Enhanced Signal Decomposition in Rolling Bearing Fault Diagnosis

Xiao Lingjun; Lü Yong; Yuan Rui

doi:10.13433/j.cnki.1003-8728.20190111

Volume 39 Issue 2

Apr. 2020

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2020 > 39(2): 165-173

Xiao Lingjun, Lü Yong, Yuan Rui. Application of MED and GMCP Sparse Enhanced Signal Decomposition in Rolling Bearing Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(2): 165-173. doi: 10.13433/j.cnki.1003-8728.20190111

Citation:

Xiao Lingjun, Lü Yong, Yuan Rui. Application of MED and GMCP Sparse Enhanced Signal Decomposition in Rolling Bearing Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(2): 165-173. doi: 10.13433/j.cnki.1003-8728.20190111

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Application of MED and GMCP Sparse Enhanced Signal Decomposition in Rolling Bearing Fault Diagnosis

doi: 10.13433/j.cnki.1003-8728.20190111

Xiao Lingjun^{1,2
,},
Lü Yong^{1,2
,
,},
Yuan Rui^1,2

1.
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
2.
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China

Received Date: 2019-03-18
Publish Date: 2020-02-05

Abstract

Abstract

In this paper, a new fault diagnosis method for rolling bearings based on minimum entropy deconvolution (MED) and generalized minimax concave penalty (GMCP) sparse enhanced signal decomposition is proposed. First, root mean square error (RMSE) is introduced to quantitatively analyze the advantages of GMCP effectively retaining periodic impact. Then MED and GMCP are applied to the fault diagnosis of rolling bearings. This method can effectively separate the noise and interference frequencies of equipment vibration signals and retain the periodic impulse frequencies completely. It is very effective for the fault diagnosis of rolling bearings, and it also has advantages in the identification of fault characteristic spectrum. The competitiveness of this method in fault diagnosis of rolling bearings is verified by simulation and experiment.
- rolling bearing,
- concave penalty,
- minimax entropy deconvolution,
- feature extraction,
- signal processing,
- fault diagnosis,
- vibration signal

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References(16)

References

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