| Autor: |
Gu Sheng, Bie Fengfeng, Miao Xinting, Zhao Wei, Guo Yue |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Jixie chuandong, Vol 47, Pp 155-162 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1004-2539 |
| DOI: |
10.16578/j.issn.1004.2539.2023.01.022 |
| Popis: |
The gearbox vibration signal contains nonlinear impact characteristics, and the significant feature information tends to be overwhelmed with other interference components. Aiming at the key issue of how to effectively extract its impact characteristics, a fault diagnosis method based on an improved extreme symmetric mode decomposition (ESMD) and support vector machine (SVM) as well as a dynamics model of spur bevel gear for investigating typically gearbox fault mechanism is proposed. In this method, the vibration signal is adaptively decomposed into multiple IMF components by the improved ESMD, and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. Meanwhile, the singular values of each selected IMF are extracted to construct the feature vector set, which is input into the SVM for the fault pattern recognition finally. Dynamic simulation and gearbox experimental research show that the improved ESMD-SVM method can extract and identify gearbox fault information effectively. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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