Fault Diagnosis of Gearboxes Based on AO-VMD and IAO-SVM
Aiming at the problems of improving the adaptability of variational mode decomposition (VMD) and in order to optimize the intrinsic mode function (IMF) and multi-fault classification, a gearbox fault diagnosis method is proposed, with which the Aquila optimizer (AO) optimizes VMD, the comprehensive...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2023-05-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.05.022 |
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| Summary: | Aiming at the problems of improving the adaptability of variational mode decomposition (VMD) and in order to optimize the intrinsic mode function (IMF) and multi-fault classification, a gearbox fault diagnosis method is proposed, with which the Aquila optimizer (AO) optimizes VMD, the comprehensive evaluation model optimizes IMF, and improves the Aquila optimizer optimization support vector machine (IAO-SVM). Firstly, AO is used to optimize the parameters of VMD and decompose the original signal. Secondly, a CRITIC-TOPSIS comprehensive evaluation model based on correlation coefficient, kurtosis, envelope entropy, energy entropy is constructed to optimize IMF, and energy entropy is extracted to establish feature vectors. Finally, it is input into IAO-SVM to identify faults. The effectiveness of this method is verified by experiments. |
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| ISSN: | 1004-2539 |