Application of Optimal Noise Parameter Ensemble Local Mean Decomposition and Spectral Kurtosis in Bearing Fault Diagnosis
In order to extract fault features of rolling bearing precisely and steadily,a method of bearing fault diagnosis,which is based on optimal noise parameters ensemble local mean decomposition（ ELMD） and spectral kurtosis（ SK） is proposed. Firstly,the relative root-mean-square error is introduced to de...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2017-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2017.05.034 |
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| Summary: | In order to extract fault features of rolling bearing precisely and steadily,a method of bearing fault diagnosis,which is based on optimal noise parameters ensemble local mean decomposition（ ELMD） and spectral kurtosis（ SK） is proposed. Firstly,the relative root-mean-square error is introduced to determine the amplitude of the optimal noise. Then,the fault signal is decomposed into a series of narrow band product functions（ PFs） by using optimal noise parameters ELMD method,and the product functions are obtained with having the highest correlation with the original vibration signal as the reconstructed signal. Finally,the method based on spectral kurtosis and envelope analysis is used to deal with the reconstructed signal. The experimental results indicate that the mode mixing can be restrained effectively by the optimal noise parameter ELMD method and fault features of rolling bearing can be extracted accurately by the approach based on optimal noise parameters ELMD and spectral kurtosis. |
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| ISSN: | 1004-2539 |