Performance Degradation Assessment Model of Rolling Bearings Combining VMD Symbol Entropy and SVDD

ZHOU Jianmin; XIONG Wenhao; YIN Wenhao; LI Jiahui; GAO Sen

doi:10.13433/j.cnki.1003-8728.20200562

Volume 42 Issue 1

Jan. 2023

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2023 > 42(1): 31-37

ZHOU Jianmin, XIONG Wenhao, YIN Wenhao, LI Jiahui, GAO Sen. Performance Degradation Assessment Model of Rolling Bearings Combining VMD Symbol Entropy and SVDD[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 31-37. doi: 10.13433/j.cnki.1003-8728.20200562

Citation:

ZHOU Jianmin, XIONG Wenhao, YIN Wenhao, LI Jiahui, GAO Sen. Performance Degradation Assessment Model of Rolling Bearings Combining VMD Symbol Entropy and SVDD[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 31-37. doi: 10.13433/j.cnki.1003-8728.20200562

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Performance Degradation Assessment Model of Rolling Bearings Combining VMD Symbol Entropy and SVDD

doi: 10.13433/j.cnki.1003-8728.20200562

ZHOU Jianmin^{1, 2
,},
XIONG Wenhao^{1, 2},
YIN Wenhao^{1, 2},
LI Jiahui^{1, 2},
GAO Sen^{1, 2}

1.
School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
2.
The Ministry of Education Key Laboratory of Conveyance and Equipment, Nanchang 330013, China

Received Date: 2020-12-03
Publish Date: 2023-01-25

Abstract

Abstract

In order to solve the problem of early fault detection of rolling bearings, a performance degradation assessment model of rolling bearings combining variable mode decomposition (VMD) symbol entropy and support vector data description (SVDD) was proposed in study. Firstly, the vibration signal is decomposed by VMD and the symbol entropy of each component is extracted. Then, the Z value of double samples is used to evaluate the symbol entropy of each component, and the feature with the largest Z value of double samples is selected as the feature vector. After feature extraction, SVDD model is finally used to evaluate performance degradation, and life cycle data is used to verify the model. The experimental results show that this model can better show the performance degradation law of rolling bearings compared with permutation entropy (PE) feature extraction method and fuzzy c-means clustering (FCM) method.
- rolling bearing,
- variational mode decomposition,
- symbol entropy,
- support vector data description,
- performance degradation assessment

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References(18)

References

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