Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA
Aiming at problems of high-speed train gearbox bearing fault signals being difficult to detect under strong noise background, and the problem that the multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) method was affected by the order of filter and the period of impulse signal, an imp...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2020-03-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2020.02.126 |
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| Summary: | Aiming at problems of high-speed train gearbox bearing fault signals being difficult to detect under strong noise background, and the problem that the multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) method was affected by the order of filter and the period of impulse signal, an improved MOMEDA method for bearing fault diagnosis based on singular value decomposition(SVD) was proposed. Firstly, SVD was used as the pre-filter of MOMEDA to filter the partial noise. Then, the fault period component was traced by MOMEDA multipoint kurtosis spectrum, and the optimal order of MOMEDA filter was solved iteratively by variable step search method. Finally, by using the periodic impulse in the signal track with MOMEDA, and the fault features with envelope spectrum were extracted. The simulation signal and the fault test data showed that this method could accurately diagnose the fault of the gearbox bearing of high-speed train, and the fault diagnosis effect was better than the complementary empirical mode decomposition method. |
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| ISSN: | 1000-128X |