Fault Feature Extraction of Rolling Bearings Based on Full Vector Improved Continuous Harmonic Wavelet Packet

QIN Qin; ZHU Fuping; YANG Fangyan; YIN Lu

doi:10.13433/j.cnki.1003-8728.20220167

Volume 42 Issue 12

Dec. 2023

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2023 > 42(12): 2040-2046

QIN Qin, ZHU Fuping, YANG Fangyan, YIN Lu. Fault Feature Extraction of Rolling Bearings Based on Full Vector Improved Continuous Harmonic Wavelet Packet[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(12): 2040-2046. doi: 10.13433/j.cnki.1003-8728.20220167

Citation:

QIN Qin, ZHU Fuping, YANG Fangyan, YIN Lu. Fault Feature Extraction of Rolling Bearings Based on Full Vector Improved Continuous Harmonic Wavelet Packet[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(12): 2040-2046. doi: 10.13433/j.cnki.1003-8728.20220167

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Fault Feature Extraction of Rolling Bearings Based on Full Vector Improved Continuous Harmonic Wavelet Packet

doi: 10.13433/j.cnki.1003-8728.20220167

School of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China

Received Date: 2021-10-07
Publish Date: 2023-12-25

Abstract

Abstract

When rolling bearings operate in rotating mechanical equipment, complex vibration signals will be measured, which can reflect equipment′s operation condition. The existing signal processing methods of rolling bearings mostly use single channel information, which can not reflect the fault state of the whole section. In this paper, a fault feature extraction method based on full vector improved continuous harmonic wavelet packet transform is proposed. Firstly, two orthogonal sensors are used to realize the dual channel vibration signal acquisition on a certain section of rolling bearing. Secondly, the collected homologous dual channel signals are fused with full vector spectrum technology. Then, the fused signal is decomposed by improved continuous harmonic wavelet packet transform. Next, the energy values that can reflect the characteristics of various faults are extracted from each subband to form a feature vector. Finally, a group of measured fault data from the rolling bearing test-bed of Case Western Reserve University in the United States are used to verify the correctness of the method.
- rolling bearing,
- full vector spectrum,
- improved continuous harmonic wavelet packet,
- feature extraction

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References(24)

References

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