Fault Feature Extraction for Rolling Bearing based on LMD Energy Entropy
In order to judge the running status of rolling bearing effectively in the case of small sample,by using the local mean decomposition（ LMD）,the rolling bearing vibration signal is decomposed. The complex multi-component signal will be decomposed into multiple single component signals. For the charac...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2019-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.01.027 |
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| Summary: | In order to judge the running status of rolling bearing effectively in the case of small sample,by using the local mean decomposition（ LMD）,the rolling bearing vibration signal is decomposed. The complex multi-component signal will be decomposed into multiple single component signals. For the characteristic that the distribution of decomposed single component signal is not uniform in the frequency range,by using the LMD energy entropy,the fault feature of rolling bearing vibration signal is extracted. The experimental results show that LMD energy entropy has a strong signal characterization capability,which can effectively extract the rolling bearing fault characteristic. |
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| ISSN: | 1004-2539 |