Research on Sparse Reconstruction of Engineering Equipment Bearing Signal under Strong Noise

ZHANG Zeyu; SHI Ze; HUI Jizhuang; REN Yu; ZHANG Xuhui

doi:10.13433/j.cnki.1003-8728.20200513

Volume 40 Issue 9

Oct. 2021

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2021 > 40(9): 1361-1369

ZHANG Zeyu, SHI Ze, HUI Jizhuang, REN Yu, ZHANG Xuhui. Research on Sparse Reconstruction of Engineering Equipment Bearing Signal under Strong Noise[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(9): 1361-1369. doi: 10.13433/j.cnki.1003-8728.20200513

Citation:

ZHANG Zeyu, SHI Ze, HUI Jizhuang, REN Yu, ZHANG Xuhui. Research on Sparse Reconstruction of Engineering Equipment Bearing Signal under Strong Noise[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(9): 1361-1369. doi: 10.13433/j.cnki.1003-8728.20200513

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Research on Sparse Reconstruction of Engineering Equipment Bearing Signal under Strong Noise

doi: 10.13433/j.cnki.1003-8728.20200513

ZHANG Zeyu^{1, 2, 3
,},
SHI Ze¹,
HUI Jizhuang^{1
,
,},
REN Yu¹,
ZHANG Xuhui²

1.
Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, Chang′ an University, Xi′an 710064, China
2.
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi′ an University of Science and Technology, Xi′an 710054, China
3.
Research Center, Tibet Tianlu Co., Ltd., Lhasa 850000, China

Received Date: 2020-04-24
Publish Date: 2021-09-05

Abstract

Abstract

The characteristics of engineering equipment bearing fault conditions are often overwhelmed by the external information. In order to effectively extract the fault data, a noise reduction filtering method combining the particle swarm optimization with the sparse reconstruction is proposed. Laplace wavelet base is selected for parameter optimization and dictionary prediction structure, and then the vibration signal of the bearing is sparsely reconstructed. By applying 2 dB Gaussian white noise to the experimental data to simulate the bearing signal in the engineering environment, the optimal sparse reconstruction algorithm is compared with the Butterworth filter and wavelet threshold denoising algorithm. The results show that the present method is more effective in terms of parameters such as peak signal-to-noise ratio and waveform similarity. The fault characteristic frequencies of the inner and outer rings of the reconstructed signal are close to the theoretical characteristic frequencies. After the noise is fully filtered, the original characteristic information provides a good data basis for the later fault diagnosis.
- Laplace wavelet,
- sparse reconstruction,
- particle swarm algorithm,
- engineering equipment bear

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References(16)

References

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