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| Wear state recognition of rolling bearings based on VMD-HMM |
| LI Yijiang1, ZHANG Jinping1, LI Yungong2 |
1.School of Mechanical Engineering, Shenyang University of Chemical Technology, Liaoning Shenyang 110142,China:
2. School of Mechanical Engineering and Automation Northeastern University, Liaoning Shenyang 110004,China |
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Abstract 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.
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Received: 12 May 2017
Published: 28 October 2018
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