FAULT FEATURE EXTRACTION OF ROLLING ELEMENT BEARINGS BASED ON ADAPTIVE MCKD
Considering the shortcomings of the maximum correlated kurtosis deconvolution（MCKD） method that cannot automatically identify the period of bearing fault impulses,exists the resampling process and the multiple input parameters,an adaptive maximum correlated kurtosis deconvolution（AMCKD） method is pr...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2020-01-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.004 |
| _version_ | 1826871860692779008 |
|---|---|
| author | CHEN BingYan SONG DongLi ZHANG WeiHua CHENG Yao LI JiaYuan |
| author_facet | CHEN BingYan SONG DongLi ZHANG WeiHua CHENG Yao LI JiaYuan |
| author_sort | CHEN BingYan |
| collection | DOAJ |
| description | Considering the shortcomings of the maximum correlated kurtosis deconvolution（MCKD） method that cannot automatically identify the period of bearing fault impulses,exists the resampling process and the multiple input parameters,an adaptive maximum correlated kurtosis deconvolution（AMCKD） method is proposed.The periodic modulation intensity（PMI） of envelope signal is used to identify the period of bearing fault impulses adaptively.Moreover,the period is constantly updated during searching for the optimal deconvolution filter iteratively,so that the real fault period is gradually approximated.Finally,the filtered signal with the largest correlated kurtosis is selected as the optimal deconvolution signal.Compared with MCKD method,AMCKD method can identify fault impulse period adaptively,avoid signal resampling process,and reduce the input parameters of the algorithm.Simulated and experimental results verify the effectiveness of this method in early fault feature extraction of rolling bearings,and the comparison with fast kurtogram method shows the superiority of AMCKD method in enhancing periodic impulse characteristics. |
| first_indexed | 2024-03-12T20:43:07Z |
| format | Article |
| id | doaj.art-6c232ce35cda42dc811590460015668f |
| institution | Directory Open Access Journal |
| issn | 1001-9669 |
| language | zho |
| last_indexed | 2025-02-16T23:47:07Z |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj.art-6c232ce35cda42dc811590460015668f2025-01-15T02:26:51ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692020-01-01421293130130609329FAULT FEATURE EXTRACTION OF ROLLING ELEMENT BEARINGS BASED ON ADAPTIVE MCKDCHEN BingYanSONG DongLiZHANG WeiHuaCHENG YaoLI JiaYuanConsidering the shortcomings of the maximum correlated kurtosis deconvolution（MCKD） method that cannot automatically identify the period of bearing fault impulses,exists the resampling process and the multiple input parameters,an adaptive maximum correlated kurtosis deconvolution（AMCKD） method is proposed.The periodic modulation intensity（PMI） of envelope signal is used to identify the period of bearing fault impulses adaptively.Moreover,the period is constantly updated during searching for the optimal deconvolution filter iteratively,so that the real fault period is gradually approximated.Finally,the filtered signal with the largest correlated kurtosis is selected as the optimal deconvolution signal.Compared with MCKD method,AMCKD method can identify fault impulse period adaptively,avoid signal resampling process,and reduce the input parameters of the algorithm.Simulated and experimental results verify the effectiveness of this method in early fault feature extraction of rolling bearings,and the comparison with fast kurtogram method shows the superiority of AMCKD method in enhancing periodic impulse characteristics.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.004Feature extractionIncipient faultRolling element bearingsAdaptive maximum correlated kurtosis deconvolutionPeriodic modulation intensity |
| spellingShingle | CHEN BingYan SONG DongLi ZHANG WeiHua CHENG Yao LI JiaYuan FAULT FEATURE EXTRACTION OF ROLLING ELEMENT BEARINGS BASED ON ADAPTIVE MCKD Jixie qiangdu Feature extraction Incipient fault Rolling element bearings Adaptive maximum correlated kurtosis deconvolution Periodic modulation intensity |
| title | FAULT FEATURE EXTRACTION OF ROLLING ELEMENT BEARINGS BASED ON ADAPTIVE MCKD |
| title_full | FAULT FEATURE EXTRACTION OF ROLLING ELEMENT BEARINGS BASED ON ADAPTIVE MCKD |
| title_fullStr | FAULT FEATURE EXTRACTION OF ROLLING ELEMENT BEARINGS BASED ON ADAPTIVE MCKD |
| title_full_unstemmed | FAULT FEATURE EXTRACTION OF ROLLING ELEMENT BEARINGS BASED ON ADAPTIVE MCKD |
| title_short | FAULT FEATURE EXTRACTION OF ROLLING ELEMENT BEARINGS BASED ON ADAPTIVE MCKD |
| title_sort | fault feature extraction of rolling element bearings based on adaptive mckd |
| topic | Feature extraction Incipient fault Rolling element bearings Adaptive maximum correlated kurtosis deconvolution Periodic modulation intensity |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.004 |
| work_keys_str_mv | AT chenbingyan faultfeatureextractionofrollingelementbearingsbasedonadaptivemckd AT songdongli faultfeatureextractionofrollingelementbearingsbasedonadaptivemckd AT zhangweihua faultfeatureextractionofrollingelementbearingsbasedonadaptivemckd AT chengyao faultfeatureextractionofrollingelementbearingsbasedonadaptivemckd AT lijiayuan faultfeatureextractionofrollingelementbearingsbasedonadaptivemckd |