Application of Optimization Parameters VMD and MED in Fault Diagnosis of Train Gearbox Rolling Bearings
Aiming at the problem of feature extraction of train gearbox rolling bearing’s incipient fault in the case of strong noise, a method of fault diagnosis based on minimum entropy deconvolution (MED) and parameter optimized variational mode decomposition (VMD) was proposed. Firstly, the bearing vibrati...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2020-05-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2020.03.030 |
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| Summary: | Aiming at the problem of feature extraction of train gearbox rolling bearing’s incipient fault in the case of strong noise, a method of fault diagnosis based on minimum entropy deconvolution (MED) and parameter optimized variational mode decomposition (VMD) was proposed. Firstly, the bearing vibration signal was denoised by using MED. Then, the VMD parameters were optimized by discrete differential evolution algorithm(DDE), and the denoising signal was processed by VMD using the optimum parameters obtained by searching, a series of intrinsic mode functions were obtained. Finally, the optimal intrinsic mode function(IMF)was selected for envelopment analysis and getting the fault frequency. The experimental results showed that the proposed method could effectively extract the fault features of train gearbox rolling bearing and could be used to rolling bearing faulf diagnosis. |
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| ISSN: | 1000-128X |