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Fault diagnosis of rolling bearing based on S transform and singular value median decomposition

Published:2022-09-22 author:ZI Yu, ZHOU Jun. Browse: 665 Check PDF documents

Fault diagnosis of rolling bearing based on S transform

and singular value median decomposition

ZI Yu, ZHOU Jun

(Faculty of Mechanical & Electrical Engineering, Kunming University of

Science & Technology, Kunming 650500, China)

Abstract: In order to effectively extract the impact characteristics of the fault signal of rolling bearing, a fault diagnosis method of rolling bearing based on S-transform time spectrum and singular value median decomposition (SVMD) algorithm was proposed.Firstly, time-frequency transformation was performed by the S transform on the vibration signal to calculate the time-frequency coefficient matrix. The time-frequency coefficient matrix was calculated through SVMD, and suitable singular values were screened for noise reduction.Then, by means of simulation,the S inverse transformation was performed on the result, the purpose of transformation was to obtain the timedomain impact characteristics of the signal. Finally, taking the outer ring and rolling element fault of rolling bearing (model N205) as an example, the impact feature extraction experiment of fault signal was carried out. The effectiveness of ST-SVMD algorithm was verified by analyzing and processing the fault data of outer ring and rolling element of bearing.The results show that,based on the ST-SVMD algorithm, the failure frequency of the outer ring of the rolling bearing is obtained, which is basically consistent with the characteristic frequency of the bearing. Based on the ST-SVMD algorithm, the fault frequency of rolling bearing is obtained, which is basically consistent with the characteristic frequency of the bearing. The results show that the ST-SVMD algorithm is effective in extracting the impact characteristics of rolling bearing fault signals.

Key words: bearing failure vibration signal; fault frequency; S transform(ST); singular value median decomposition(SVMD); impact characteristics extraction; signal noise reduction

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