Fault feature extraction method for rolling bearing based on MVMD and complex Fourier transform
| Autor: | Chuanjin Huang, Haijun Song |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Vibroengineering. 25:269-289 |
| ISSN: | 2538-8460 1392-8716 |
| DOI: | 10.21595/jve.2022.22673 |
| Popis: | The vibration signals caused by rolling bearing defects in different directions may be different, and the fault diagnosis based on single channel vibration signals may be made incorrectly, and the observation results may be understood wrong. To avoid it, a new rolling bearing fault feature extraction method based on multivariate variational mode decomposition (MVMD) and complex Fourier transform (CFT) were proposed. First, the orthogonally sampled vibration signals were combined into a multivariate signal, and the multivariate signal was decomposed into several intrinsic mode functions (IMFs) using the MVMD. As per this method, a unified mathematical model was used to model vibration signals in two directions, ensuring that fault features were decomposed to the same level. Finally, the CFT was applied to fuse the envelope signals in two directions in order to obtain a clearer and comprehensive amplitude-frequency feature. Simulation and test results verify the feasibility and superiority of the proposed method. |
| Databáze: | OpenAIRE |
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