Gear fault diagnosis based on SSWPT marginal spectrum feature information extraction

被引：0
|
作者：
Tang G. [1 ]
Xu Z. [1 ]
Pang B. [2 ]
Bai J. [1 ]
机构：
[1] Hebei Key Laboratory of Electric Machinery Health Maintenance & Failure Prevention, North China Electric Power University, Baoding
[2] College of Quality and Technical Supervision, Hebei University, Baoding
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2022年 / 41卷 / 14期
关键词：
fault diagnosis; gear; marginal spectrum; synchrosqueezed wave packet transform (SSWPT);
D O I：
10.13465/j.cnki.jvs.2022.14.007
中图分类号：
学科分类号：
摘要：
Under the influence of noise, gear fault information is difficult to be identified. As a new time-frequency analysis method, synchrosqueezed wave packet transform (SSWPT) has good ability to restrain noise effect. A gear fault diagnosis method based on SSWPT marginal spectrum feature extraction was proposed. Firstly, the vibration signal of gear fault was transformed into an energy matrix by SSWPT and the marginal spectrum of the gear vibration signal was obtained by the integration of the energy matrix. Then, the meshing frequency and its multipliers were extracted by the marginal spectrum of SSWPT and the energy matrixes were reconstructed through inverse synchrosqueezed wave packet transformation (ISSWPT). Finally, the reconstructed signal was demodulated and analysed, and the fault features of the gear were effectively extracted. The simulated and experimental results show that the proposed method is better than the envelope spectrum method and the resonance demodulation method based on fast kurtogram. It can accurately extract gear fault feature information and provides an effective way for gear fault diagnosis. © 2022 Chinese Vibration Engineering Society. All rights reserved.
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页码：50 / 57
页数：7
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