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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
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86-571-87239571
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No.9 Gaoguannong,Daxue Road,Hangzhou,China
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meem_contribute@163.com
Abstract: Aiming at the problem in identifying impact fault features of rolling bearings due to interference factors such as noise, a new method based on similar segmented collaborative filtering (SSCF) and time-reassigned synchro extracting transform (TSET) of the rolling bearing fault diagnosis method was proposed. Firstly, SSCF was used to divide similar segments of the signal, and then the similar segments were arranged into a two-dimensional array, so that Savitzky-Golay filter and polynomial fitting algorithm could be utilized to respectively filter them in the X and Y directions. After that the signals were reconstructed as well as the noise reduction processing was realized. Then, based on the theoretical framework of synchronous compression transformation, TSET was used to estimate the group delay and rearrange the time-frequency energy in the time direction, so as to obtain a clear and concentrated time-frequency representation of the impact characteristics. Finally, by calculating the time-frequency the characteristic frequency of the fault, was determined by the time interval between the ridges, which enabled the fault diagnosis of the rolling bearing. The numerical simulation signal and the bearing fault data of the test bench were also analyzed and verified. The experiment results illustrate that,the maximum correlation coefficient after SSCF denoising is 0.825 5, which is 0.250 4 higher than the wavelet threshold denoising, and greatly highlight the impact characteristics. The Renyi entropy of TSET algorithm is the lowest, which is 11.286 1, it is 4.386 2 lower than that of SST, and the time-frequency expression with more concentrated energy is obtained. The research results show that the method based on SSCF and TSET is highly effective for the fault diagnosis of rolling bearings under strong noise.
Key words: similar segments collaborative filtering(SSCF); time-reassigned synchro extracting transform(TSET); feature extraction; noise reduction processing;signal reconfiguration;time and frequency analysis
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