Early Fault Extraction of Rolling Bearing based on LMD and MCKD
When the roller bearings are in the early stage of failure,the characteristic signal is weak and it is affected by the interference noise,which makes the fault feature difficult to extract. In order to solve this problem,a fault diagnosis method based on the combination of local mean decomposition（...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2018-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.03.025 |
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| Summary: | When the roller bearings are in the early stage of failure,the characteristic signal is weak and it is affected by the interference noise,which makes the fault feature difficult to extract. In order to solve this problem,a fault diagnosis method based on the combination of local mean decomposition（ LMD） and maximum correlated kurtosis deconvolution（ MCKD） is proposed. Under the strong noise environment,LMD is difficult to extract the characteristic of weak fault signal,therefore,a set of PF components that are decomposed by LMD which use the correlation coefficient and kurtosis value select sensitive component for signal reconstruction. Then MCKD is used to filter and it is used to highlight the pulse of fault signal. Finally,according to the signal envelope power spectrum,the fault characteristic frequency is extracted,the effectiveness of the method is proved by some simulation and application examples. |
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| ISSN: | 1004-2539 |