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Fault diagnosis of rolling bearings based on SVD and SET
Published:2022-02-23 author:YU Xiang, FANG Yu-feng, GAO Yu,et al. Browse: 725 Check PDF documents
Fault diagnosis of rolling bearings based on SVD and SET


YU Xiang, FANG Yu-feng, GAO Yu, CHEN Guang, XIA Lin-qin

(School of Additive Manufacturing,Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China)


Abstract: In engineering, the collected bearing vibration signal often contained a lot of background noise. Aiming at the problem that the synchro extractingtransform (SET) could not clearly and accurately express the characteristics of the rolling bearing vibration signal in the time-frequency spectrum,combining with the advantages of singular value decomposition (SVD) in noise reduction, a fault diagnosis method based on SVD and SET was proposed.Firstly, the one-dimensional vibration signal was converted into the two-dimensional time-frequency plane through SET, and the time-frequency spectrum was obtained.Then SVD was used to decompose the time-frequency spectrum, and reconstruct the time-frequency spectrum according to the size of the singular value. At this time, the spectrogram could clearly and accurately indicate the time-frequency characteristics of the signal. Finally, returning to the time domain through the inverse transformation, the time domain signal was demodulated and enveloped, and the fault characteristic frequency of the rolling bearing and its frequency multiplication could be clearly expressed by the obtained envelope spectrum.The experimental results show that, comparing with only SET for identifying the rolling bearing vibration signal, the background noise can be effectively removed by this method in the time-frequency spectrum, and its envelope spectrum can highlight the characteristic frequency of the fault, thereby enabling effective fault diagnosis.

Key words: rolling bearing; fault diagnosis; synchro extracting transform (SET); singular value decomposition (SVD); envelope spectrum



YU Xiang, FANG Yu-feng, GAO Yu,et al. 
Fault diagnosis of rolling bearings based on SVD and SET[J].Journal of Mechanical & Electrical Engineering,2021,38(12):1586-1591,1604.




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