Application of Improved EWT Method in Bearing Fault Diagnosis of High-speed Train
It is difficult to extract fault features of axle box bearings of high-speed train. To solve this problem, an improved empirical wavelet transform based on spectrum trend and frequency band combination was proposed and applied to the fault diagnosis of high-speed train bearings. Firstly, by using em...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2020-07-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2020.04.010 |
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| Summary: | It is difficult to extract fault features of axle box bearings of high-speed train. To solve this problem, an improved empirical wavelet transform based on spectrum trend and frequency band combination was proposed and applied to the fault diagnosis of high-speed train bearings. Firstly, by using empirical mode decomposition, the spectrum trend of the fault signal according to the IMF component judgment criterion was extracted, and the initial spectrum boundary point was obtained. Then, the fault information judgment index of each initial frequency band was calculated, and the adaptive threshold was obtained to determine the initial frequency band, the validity of the spectrum was re-divided by combining the invalid frequency bands. Finally, the empirical wavelet transform was carried out to pass each frequency band through the orthogonal filter bank, and the obtained component signals were subjected to Hilbert transform to obtain the fault characteristic frequency of the bearing. Through simulation and experimental verification, the improved EWT method could accurately extract the fundamental frequency and frequency doubling component of the bearing fault characteristic frequency, and effectively determine the bearing fault. |
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| ISSN: | 1000-128X |