Feature Extraction of Weak Fault for Rolling Bearing based on Improved SSD Denoising
Aiming at the problem of early weak fault features of rolling bearings are difficult to be extracted under strong background noise and the components decomposed by the singular spectral decomposition method still contain noise，a method of extracting the weak fault features of rolling bearing based o...
| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2022-03-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.03.025 |
| Summary: | Aiming at the problem of early weak fault features of rolling bearings are difficult to be extracted under strong background noise and the components decomposed by the singular spectral decomposition method still contain noise，a method of extracting the weak fault features of rolling bearing based on the combination of singular spectrum decomposition （SSD） and maximum cyclostationarity blind deconvolution （CYCBD） is proposed. The SSD method is used to adaptively decompose the bearing vibration signal into high-frequency to low-frequency singular spectral components. The best component is selected according to the principle of maximum component kurtosis. The best component is used in CYCBD post-processing for further noise reduction. Furthermore，the noise reduced signal is analyzed by Hilbert envelope demodulation to obtain the fault characteristic frequency. Simulation and experimental analysis show that this method can extract early weak fault features of rolling bearings effectively. |
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| ISSN: | 1004-2539 |