RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT)
Aiming at the problem that the vibration signal of rolling bearing is difficult to extract due to the characteristics of non-linear, non-stationary and low signal-to-noise ratio, a new fault extraction method based on symplectic geometry mode decomposition(SGMD) and multipoint optimal minimum entrop...
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Published in | Ji xie qiang du pp. 1279 - 1285 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | Chinese |
Published |
Editorial Office of Journal of Mechanical Strength
01.01.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Aiming at the problem that the vibration signal of rolling bearing is difficult to extract due to the characteristics of non-linear, non-stationary and low signal-to-noise ratio, a new fault extraction method based on symplectic geometry mode decomposition(SGMD) and multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) theory is proposed. Firstly, a list of symplectic geometry components(SGCs) are obtained with SGMD decomposing the fault signal; secondly, SGCs are selected for signal reconstruction according to the correlation criterion, then, MOMEDA decomposition parameters are determined; finally, the reconstructed signal is processed with MOMEDA for enhancing the signal-to-nosise ratio, and envelope spectrum analysis is utilized to extract fault features. Simulated and experimental results verify that SGMD-MOMEDA can accurately extract the fault frequency of rolling bearings, and the comparison with the Empirical Mode Decomposition(EMD) shows that the SGMD is more accurate when reconstructing s |
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ISSN: | 1001-9669 |