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...

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Bibliographic Details
Published inJixie Chuandong Vol. 46; pp. 163 - 169
Main Authors Xupeng Wang, Huer Sun
Format Journal Article
LanguageChinese
Published Editorial Office of Journal of Mechanical Transmission 01.03.2022
Subjects
Rolling bearing;Singular spectrum decomposition;Maximum second-order cyclostationarity blind deconvolution;Weak fault;Feature extraction
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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.
ISSN:1004-2539