Extraction of the Early Fault Feature of Rolling Bearing based on MED-RSSD
During the early failure of the rolling bearing,the fault signal is very weak and the fault information is difficult to be extracted because of the influence of the background noise,in order to be able to effectively detect the bearing failure. A new diagnosis approach which combines minimum entropy...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2018-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.06.025 |
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| _version_ | 1841547438745190400 |
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| author | Yang Bin Zhang Jiawei Wang Jianguo Zhang Chao Qin Bo |
| author_facet | Yang Bin Zhang Jiawei Wang Jianguo Zhang Chao Qin Bo |
| author_sort | Yang Bin |
| collection | DOAJ |
| description | During the early failure of the rolling bearing,the fault signal is very weak and the fault information is difficult to be extracted because of the influence of the background noise,in order to be able to effectively detect the bearing failure. A new diagnosis approach which combines minimum entropy deconvolution（MED） and resonance sparse signal decomposition（RSSD） is put forward. Firstly,the MED is used to reduce noise signal of the bearing fault vibration signal. Then the RSSD is used to decompose the signal into the high resonance component containing the harmonic signal and the low resonance component containing the transient impact signal. Finally,the envelope power spectrum is used to extract the fault characteristic frequency from the low resonant component. Through simulation and experiment,this method is suitable for weak fault feature extraction. |
| format | Article |
| id | doaj-art-bd660a9bffa44578a04ad9513a05f5d7 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-bd660a9bffa44578a04ad9513a05f5d72025-01-10T14:42:01ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392018-01-014212012429937271Extraction of the Early Fault Feature of Rolling Bearing based on MED-RSSDYang BinZhang JiaweiWang JianguoZhang ChaoQin BoDuring the early failure of the rolling bearing,the fault signal is very weak and the fault information is difficult to be extracted because of the influence of the background noise,in order to be able to effectively detect the bearing failure. A new diagnosis approach which combines minimum entropy deconvolution（MED） and resonance sparse signal decomposition（RSSD） is put forward. Firstly,the MED is used to reduce noise signal of the bearing fault vibration signal. Then the RSSD is used to decompose the signal into the high resonance component containing the harmonic signal and the low resonance component containing the transient impact signal. Finally,the envelope power spectrum is used to extract the fault characteristic frequency from the low resonant component. Through simulation and experiment,this method is suitable for weak fault feature extraction.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.06.025Rolling bearingFault diagnosisMinimum entropy deconvolutionResonance sparse signal decomposition |
| spellingShingle | Yang Bin Zhang Jiawei Wang Jianguo Zhang Chao Qin Bo Extraction of the Early Fault Feature of Rolling Bearing based on MED-RSSD Jixie chuandong Rolling bearing Fault diagnosis Minimum entropy deconvolution Resonance sparse signal decomposition |
| title | Extraction of the Early Fault Feature of Rolling Bearing based on MED-RSSD |
| title_full | Extraction of the Early Fault Feature of Rolling Bearing based on MED-RSSD |
| title_fullStr | Extraction of the Early Fault Feature of Rolling Bearing based on MED-RSSD |
| title_full_unstemmed | Extraction of the Early Fault Feature of Rolling Bearing based on MED-RSSD |
| title_short | Extraction of the Early Fault Feature of Rolling Bearing based on MED-RSSD |
| title_sort | extraction of the early fault feature of rolling bearing based on med rssd |
| topic | Rolling bearing Fault diagnosis Minimum entropy deconvolution Resonance sparse signal decomposition |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.06.025 |
| work_keys_str_mv | AT yangbin extractionoftheearlyfaultfeatureofrollingbearingbasedonmedrssd AT zhangjiawei extractionoftheearlyfaultfeatureofrollingbearingbasedonmedrssd AT wangjianguo extractionoftheearlyfaultfeatureofrollingbearingbasedonmedrssd AT zhangchao extractionoftheearlyfaultfeatureofrollingbearingbasedonmedrssd AT qinbo extractionoftheearlyfaultfeatureofrollingbearingbasedonmedrssd |