Wear state recognition of rolling bearings based on VMD-HMM

被引：0

作者：

Li Y. ^{[1
]}

Zhang J. ^{[1
]}

Li Y. ^{[1
]}

机构：

[1] School of Mechanical Engineering, Shenyang University of Chemical Technology, Shenyang

[2] School of Mechanical Engineering and Automation, Northeastern University, Shenyang

来源：

Zhang, Jinping | 2018年 / Chinese Vibration Engineering Society卷 / 37期

关键词：

Fault diagnosis; Hidden Markov model (HMM); Rolling bearing; Variational mode decomposition (VMD);

D O I：

10.13465/j.cnki.jvs.2018.21.010

中图分类号：

学科分类号：

摘要：

Based on good performance of the variational mode decomposition (VMD) in signal processing and classification ability of the hidden Markov model (HMM) to time series, a rolling bearing wear state recognition method based on VMD-HMM was proposed. Firstly, VMD was used to decompose vibration signals of a bearing in its various wear durations, and the energy entropy of each IMF after VMD was calculated. Then, various IMFs' energy entropies of bearing vibration signals in various wear durations were extracted to form eigenvector sequences. Finally, the randomly selected 20 groups in total 80 groups of eigenvector sequences for each wear state were input into HMM model to be trained, and the rest eigenvector sequences were tested. Through comparing logarithmic likelihood probability values, the bearing wear state was determined. The test results showed that the proposed method can be used to accurately distinguish the wear state of the bearing; compared with EMD-HMM and the harmonic wavelet sample entropy HMM model, it has higher recognition and accuracy. © 2018, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：61 / 67

页数：6

共 13 条

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