ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE
According to characteristics of the bearing signal,the second-order cyclic demodulation information was introduced into machine learning,and a multi-domain kernel extreme learning machine（MKELM） based on the combination of cyclic autocorrelation（CAF） frequency domain features and time domain feature...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2020-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.005 |
| _version_ | 1797767680024903680 |
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| author | WANG XiaoHui WANG GuangBing XIANG JiaWei HUANG Zhen SUI GuangZhou |
| author_facet | WANG XiaoHui WANG GuangBing XIANG JiaWei HUANG Zhen SUI GuangZhou |
| author_sort | WANG XiaoHui |
| collection | DOAJ |
| description | According to characteristics of the bearing signal,the second-order cyclic demodulation information was introduced into machine learning,and a multi-domain kernel extreme learning machine（MKELM） based on the combination of cyclic autocorrelation（CAF） frequency domain features and time domain features（TD） was proposed to accurately identify the bearing status.The algorithm constructed a CAF function based on the second-order cyclic characteristics of the bearing signal to extract the cyclic frequency domain features of the samples,then combined them with the time domain feature quantities of the samples.The matching factors of multi-domain feature vectors was designed to fuse TD and CAF feature vectors; finally,the fused CAF-TD sample features was input into the kernel extreme learning machine for cluster regression.The spindle bearing experimental results show that the cyclic frequency domain statistics extracted based on CAF can sensitively reflect the signal characteristics.Compared with the classic classifier,the CAF-TD multi-domain kernel extreme learning machine can extract more feature information from limited samples and obtain more accurate diagnostic result. |
| first_indexed | 2024-03-12T20:43:18Z |
| format | Article |
| id | doaj.art-9e1077b5573e484da5223dbd06bfecb2 |
| institution | Directory Open Access Journal |
| issn | 1001-9669 |
| language | zho |
| last_indexed | 2024-03-12T20:43:18Z |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj.art-9e1077b5573e484da5223dbd06bfecb22023-08-01T07:52:19ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692020-01-01421302130930609343ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINEWANG XiaoHuiWANG GuangBingXIANG JiaWeiHUANG ZhenSUI GuangZhouAccording to characteristics of the bearing signal,the second-order cyclic demodulation information was introduced into machine learning,and a multi-domain kernel extreme learning machine（MKELM） based on the combination of cyclic autocorrelation（CAF） frequency domain features and time domain features（TD） was proposed to accurately identify the bearing status.The algorithm constructed a CAF function based on the second-order cyclic characteristics of the bearing signal to extract the cyclic frequency domain features of the samples,then combined them with the time domain feature quantities of the samples.The matching factors of multi-domain feature vectors was designed to fuse TD and CAF feature vectors; finally,the fused CAF-TD sample features was input into the kernel extreme learning machine for cluster regression.The spindle bearing experimental results show that the cyclic frequency domain statistics extracted based on CAF can sensitively reflect the signal characteristics.Compared with the classic classifier,the CAF-TD multi-domain kernel extreme learning machine can extract more feature information from limited samples and obtain more accurate diagnostic result.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.005Bearing;Cyclic autocorrelation;Kernel extreme learning machine;Pattern recognition |
| spellingShingle | WANG XiaoHui WANG GuangBing XIANG JiaWei HUANG Zhen SUI GuangZhou ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE Jixie qiangdu Bearing;Cyclic autocorrelation;Kernel extreme learning machine;Pattern recognition |
| title | ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE |
| title_full | ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE |
| title_fullStr | ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE |
| title_full_unstemmed | ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE |
| title_short | ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE |
| title_sort | roller bearing fault recognition method based on cyclic autocorrelation and multi domain kernel limit learning machine |
| topic | Bearing;Cyclic autocorrelation;Kernel extreme learning machine;Pattern recognition |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.005 |
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