Application of Variational Mode Decomposition based on the FOA and in Bearing Fault Diagnosis
Abstract The variational mode decomposition （VMD） is widely used in fault diagnosis. Extracting fault characteristics from vibration signals is a critical part in the bearing fault diagnosis. It is difficult to extract early fault signatures under strong background noise and pulse interference. Thus...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2020-05-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2020.05.024 |
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| Summary: | Abstract The variational mode decomposition （VMD） is widely used in fault diagnosis. Extracting fault characteristics from vibration signals is a critical part in the bearing fault diagnosis. It is difficult to extract early fault signatures under strong background noise and pulse interference. Thus， a new fault diagnosis method is proposed based on the variational mode decomposition and fruit fly optimization algorithm （FOA）. Firstly， the fruit fly optimization algorithm is used to optimize the penalty parameter α and the decomposition number K of the VMD adaptively to obtain the optimal parameter combination. Then， the VMD decomposition of the signal is performed to find K modal components. Finally， the optimal modal component is selected based on the kurtosis maximization criterion for envelope demodulation analysis and extracting the frequency of the fault feature. The effectiveness of the presented method is verified by simulation signal analysis， detecting bearing fault signal and comparing with multi-resolution singular value decomposition （MRSVD） approach based on fruit fly optimization algorithm. |
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| ISSN: | 1004-2539 |