Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification
In view of the nonlinear,non-stationary and massive noise features of reciprocating pump power of rolling bearing vibration signal,a fault diagnosis method of rolling bearing based on EEMD,distance factor,correlation coefficient and wavelet packet decomposition is proposed. By measuring the vibratio...
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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.1004.2539.2018.04.030 |
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| author | Pei Junfeng Sun Jianhua Song Chuanzhi Song Yupeng Ge Huizhong |
| author_facet | Pei Junfeng Sun Jianhua Song Chuanzhi Song Yupeng Ge Huizhong |
| author_sort | Pei Junfeng |
| collection | DOAJ |
| description | In view of the nonlinear,non-stationary and massive noise features of reciprocating pump power of rolling bearing vibration signal,a fault diagnosis method of rolling bearing based on EEMD,distance factor,correlation coefficient and wavelet packet decomposition is proposed. By measuring the vibration signals of bearings in the bearing life test and decomposing the signals by using the method of EEMD,a number of IMF components is got. Then,the energy characteristic signal vectors is obtained through wavelet packet decomposing and constructing of reconstructed vibration signals,which got from the selecting and reconstructing of IMF components using the method of combination of distance factor and correlation coefficient. At last,the type of failure is determined based on the absolute value of the difference in the correlation coefficient of the energy characteristic signal vectors. Compared with the direct correlation coefficient analysis of the bearing vibration signal,this method has a high fault recognition rate,and doesn’t require a large amount of data training needed by neural networks,it is a better bearing fault identification method. |
| format | Article |
| id | doaj-art-2c705320882347eb9b97a72a1d2ab770 |
| 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-2c705320882347eb9b97a72a1d2ab7702025-01-10T14:42:40ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392018-01-014215015529936110Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient IdentificationPei JunfengSun JianhuaSong ChuanzhiSong YupengGe HuizhongIn view of the nonlinear,non-stationary and massive noise features of reciprocating pump power of rolling bearing vibration signal,a fault diagnosis method of rolling bearing based on EEMD,distance factor,correlation coefficient and wavelet packet decomposition is proposed. By measuring the vibration signals of bearings in the bearing life test and decomposing the signals by using the method of EEMD,a number of IMF components is got. Then,the energy characteristic signal vectors is obtained through wavelet packet decomposing and constructing of reconstructed vibration signals,which got from the selecting and reconstructing of IMF components using the method of combination of distance factor and correlation coefficient. At last,the type of failure is determined based on the absolute value of the difference in the correlation coefficient of the energy characteristic signal vectors. Compared with the direct correlation coefficient analysis of the bearing vibration signal,this method has a high fault recognition rate,and doesn’t require a large amount of data training needed by neural networks,it is a better bearing fault identification method.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.04.030EEMDWavelet packet decompositionDistance factorCorrelation coefficientRolling bearingFault diagnosis |
| spellingShingle | Pei Junfeng Sun Jianhua Song Chuanzhi Song Yupeng Ge Huizhong Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification Jixie chuandong EEMD Wavelet packet decomposition Distance factor Correlation coefficient Rolling bearing Fault diagnosis |
| title | Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification |
| title_full | Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification |
| title_fullStr | Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification |
| title_full_unstemmed | Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification |
| title_short | Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification |
| title_sort | rolling bearing fault diagnosis method based on eemd denoising and correlation coefficient identification |
| topic | EEMD Wavelet packet decomposition Distance factor Correlation coefficient Rolling bearing Fault diagnosis |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.04.030 |
| work_keys_str_mv | AT peijunfeng rollingbearingfaultdiagnosismethodbasedoneemddenoisingandcorrelationcoefficientidentification AT sunjianhua rollingbearingfaultdiagnosismethodbasedoneemddenoisingandcorrelationcoefficientidentification AT songchuanzhi rollingbearingfaultdiagnosismethodbasedoneemddenoisingandcorrelationcoefficientidentification AT songyupeng rollingbearingfaultdiagnosismethodbasedoneemddenoisingandcorrelationcoefficientidentification AT gehuizhong rollingbearingfaultdiagnosismethodbasedoneemddenoisingandcorrelationcoefficientidentification |