Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA
Aiming at the problem that the early periodic transient impulse of rolling bearings is not obvious and the spectral kurtosis is poorly analyzed under low signal-to-noise ratio， a method of extracting the weak fault features of rolling bearing based on the combination of multipoint optimal minimum en...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2021-02-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.02.024 |
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| _version_ | 1841547230683594752 |
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| author | Fuwang Liang Huer Sun Kexin Liu |
| author_facet | Fuwang Liang Huer Sun Kexin Liu |
| author_sort | Fuwang Liang |
| collection | DOAJ |
| description | Aiming at the problem that the early periodic transient impulse of rolling bearings is not obvious and the spectral kurtosis is poorly analyzed under low signal-to-noise ratio， a method of extracting the weak fault features of rolling bearing based on the combination of multipoint optimal minimum entropy deconvolution adjusted （MOMEDA） and spectral kurtosis is proposed. Firstly， MOMEDA is used as the prefilter to reduce the noise of weak fault impulse signal with strong noise and highlight the periodic impulse component in the signal. Then， through spectral kurtosis analysis， the denoised signal is filtered under the optimal center frequency and bandwidth. Finally， the fault characteristic frequency of bearing signal can be accurately obtained by Hilbert envelope spectrum analysis of filtered signal. The simulation and experimental results show that the method can effectively enhance the periodic transient impulse characteristics of vibration signals and extract the early weak fault characteristics of rolling bearing. |
| format | Article |
| id | doaj-art-058f417cbbaf481180efa7629af69159 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2021-02-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-058f417cbbaf481180efa7629af691592025-01-10T14:54:09ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392021-02-014515716229799742Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDAFuwang LiangHuer SunKexin LiuAiming at the problem that the early periodic transient impulse of rolling bearings is not obvious and the spectral kurtosis is poorly analyzed under low signal-to-noise ratio， a method of extracting the weak fault features of rolling bearing based on the combination of multipoint optimal minimum entropy deconvolution adjusted （MOMEDA） and spectral kurtosis is proposed. Firstly， MOMEDA is used as the prefilter to reduce the noise of weak fault impulse signal with strong noise and highlight the periodic impulse component in the signal. Then， through spectral kurtosis analysis， the denoised signal is filtered under the optimal center frequency and bandwidth. Finally， the fault characteristic frequency of bearing signal can be accurately obtained by Hilbert envelope spectrum analysis of filtered signal. The simulation and experimental results show that the method can effectively enhance the periodic transient impulse characteristics of vibration signals and extract the early weak fault characteristics of rolling bearing.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.02.024Rolling bearingMultipoint optimal minimum entropy deconvolution adjustedSpectral KurtosisWeak faultFeature extraction |
| spellingShingle | Fuwang Liang Huer Sun Kexin Liu Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA Jixie chuandong Rolling bearing Multipoint optimal minimum entropy deconvolution adjusted Spectral Kurtosis Weak fault Feature extraction |
| title | Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA |
| title_full | Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA |
| title_fullStr | Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA |
| title_full_unstemmed | Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA |
| title_short | Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA |
| title_sort | feature extraction of weak fault for rolling bearing based on spectral kurtosis and momeda |
| topic | Rolling bearing Multipoint optimal minimum entropy deconvolution adjusted Spectral Kurtosis Weak fault Feature extraction |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.02.024 |
| work_keys_str_mv | AT fuwangliang featureextractionofweakfaultforrollingbearingbasedonspectralkurtosisandmomeda AT huersun featureextractionofweakfaultforrollingbearingbasedonspectralkurtosisandmomeda AT kexinliu featureextractionofweakfaultforrollingbearingbasedonspectralkurtosisandmomeda |