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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2022-01-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.06.02 |
| _version_ | 1797767618814279680 |
|---|---|
| author | CAO YaLei DU YingJun WEI Guang DONG XinMin GAO LiPeng LIU YuXi |
| author_facet | CAO YaLei DU YingJun WEI Guang DONG XinMin GAO LiPeng LIU YuXi |
| author_sort | CAO YaLei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T20:41:26Z |
| format | Article |
| id | doaj.art-ae03fcde3cb2456989cbffb0ebeddb40 |
| institution | Directory Open Access Journal |
| issn | 1001-9669 |
| language | zho |
| last_indexed | 2024-03-12T20:41:26Z |
| publishDate | 2022-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj.art-ae03fcde3cb2456989cbffb0ebeddb402023-08-01T07:55:16ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692022-01-011279128536351156RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT)CAO YaLeiDU YingJunWEI GuangDONG XinMinGAO LiPengLIU YuXiAiming 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.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.06.02Symplectic geometry mode decomposition;Symplectic geometry component;Multipoint optimal minimum entropy deconvolution adjusted;Feature extraction;Rolling bearing fault diagnosis |
| spellingShingle | CAO YaLei DU YingJun WEI Guang DONG XinMin GAO LiPeng LIU YuXi RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT) Jixie qiangdu Symplectic geometry mode decomposition;Symplectic geometry component;Multipoint optimal minimum entropy deconvolution adjusted;Feature extraction;Rolling bearing fault diagnosis |
| title | RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT) |
| title_full | RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT) |
| title_fullStr | RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT) |
| title_full_unstemmed | RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT) |
| title_short | RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT) |
| title_sort | research on rolling bearing fault feature extraction method with sgmd momeda mt |
| topic | Symplectic geometry mode decomposition;Symplectic geometry component;Multipoint optimal minimum entropy deconvolution adjusted;Feature extraction;Rolling bearing fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.06.02 |
| work_keys_str_mv | AT caoyalei researchonrollingbearingfaultfeatureextractionmethodwithsgmdmomedamt AT duyingjun researchonrollingbearingfaultfeatureextractionmethodwithsgmdmomedamt AT weiguang researchonrollingbearingfaultfeatureextractionmethodwithsgmdmomedamt AT dongxinmin researchonrollingbearingfaultfeatureextractionmethodwithsgmdmomedamt AT gaolipeng researchonrollingbearingfaultfeatureextractionmethodwithsgmdmomedamt AT liuyuxi researchonrollingbearingfaultfeatureextractionmethodwithsgmdmomedamt |