Feature Extraction of Weak Fault of Rolling Bearing based on TVF-EMD and TEO
Aiming at the problem that the vibration signals for rotating machinery rotors are usually accompanied by strong noise， it is difficult to extract its effective information. A method of fault feature extraction based on time-varying filter empirical mode decomposition （TVF-EMD） and Teager energy ope...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2021-03-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.03.027 |
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| Summary: | Aiming at the problem that the vibration signals for rotating machinery rotors are usually accompanied by strong noise， it is difficult to extract its effective information. A method of fault feature extraction based on time-varying filter empirical mode decomposition （TVF-EMD） and Teager energy operator （TEO） is proposed. Firstly， the TVF-EMD method is used to adaptively decompose the bearing vibration signal to obtain a set of intrinsic modal functions. Then， the kurtosis calculation is performed on the decomposition result， and the sensitive component is selected with the highest kurtosis value according to the maximum kurtosis criterion. Furthermore， the Teager energy operator is used to demodulate the selected sensitive components， and the weak fault feature extraction of the bearing is realized by observing the obvious periodic fault feature frequency. Simulations and experiments are carried out， and the results prove that this method can effectively diagnose the weak faults of bearings. |
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| ISSN: | 1004-2539 |