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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 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 signals. This method has certain application value in the field of fault diagnosis.
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 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 signals. This method has certain application value in the field of fault diagnosis.
RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT)
CAO YaLei (author) / DU YingJun (author) / WEI Guang (author) / DONG XinMin (author) / GAO LiPeng (author) / LIU YuXi (author)
2022
Article (Journal)
Electronic Resource
Unknown
Symplectic geometry mode decomposition , Symplectic geometry component , Multipoint optimal minimum entropy deconvolution adjusted , Feature extraction , Rolling bearing fault diagnosis , Mechanical engineering and machinery , TJ1-1570 , Materials of engineering and construction. Mechanics of materials , TA401-492
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