Rolling Bearing Degradation State Identification Based on LPP Optimized by GA
In view of the problem that the actual degradation status of rolling bearing has a poor distinguishing characteristic and strong fuzziness, a rolling bearing degradation state identification method based on multidomain feature fusion and dimension reduction of manifold learning combined with GG clus...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | International Journal of Rotating Machinery |
| Online Access: | http://dx.doi.org/10.1155/2016/9281098 |
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| _version_ | 1832550290235064320 |
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| author | He Yu Hong-ru Li Zai-ke Tian Wei-guo Wang |
| author_facet | He Yu Hong-ru Li Zai-ke Tian Wei-guo Wang |
| author_sort | He Yu |
| collection | DOAJ |
| description | In view of the problem that the actual degradation status of rolling bearing has a poor distinguishing characteristic and strong fuzziness, a rolling bearing degradation state identification method based on multidomain feature fusion and dimension reduction of manifold learning combined with GG clustering is proposed. Firstly, the rolling bearing all-life data is preprocessed by local characteristic-scale decomposition (LCD) and six typical features including relative energy spectrum entropy (LREE), relative singular spectrum entropy (LRSE), two-element multiscale entropy (TMSE), standard deviation (STD), RMS, and root-square amplitude (XR) are extracted and compose the original multidomain feature set. And then, locally preserving projection (LPP) is utilized to reduce dimension of original fusion feature set and genetic algorithm is applied to optimize the process of feature fusion. Finally, fuzzy recognition of rolling bearing degradation state is carried out by GG clustering and the principle of maximum membership degree and excellent performance of the proposed method is validated by comparing the recognition accuracy of LPP and GA-LPP. |
| format | Article |
| id | doaj-art-87acce0c8ec24b10bd49f084e30bd448 |
| institution | Kabale University |
| issn | 1023-621X 1542-3034 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Rotating Machinery |
| spelling | doaj-art-87acce0c8ec24b10bd49f084e30bd4482025-02-03T06:07:10ZengWileyInternational Journal of Rotating Machinery1023-621X1542-30342016-01-01201610.1155/2016/92810989281098Rolling Bearing Degradation State Identification Based on LPP Optimized by GAHe Yu0Hong-ru Li1Zai-ke Tian2Wei-guo Wang3Mechanical Engineering College, No. 97, Heping West Road, Shijiazhuang 050003, ChinaMechanical Engineering College, No. 97, Heping West Road, Shijiazhuang 050003, ChinaMechanical Engineering College, No. 97, Heping West Road, Shijiazhuang 050003, ChinaMechanical Engineering College, No. 97, Heping West Road, Shijiazhuang 050003, ChinaIn view of the problem that the actual degradation status of rolling bearing has a poor distinguishing characteristic and strong fuzziness, a rolling bearing degradation state identification method based on multidomain feature fusion and dimension reduction of manifold learning combined with GG clustering is proposed. Firstly, the rolling bearing all-life data is preprocessed by local characteristic-scale decomposition (LCD) and six typical features including relative energy spectrum entropy (LREE), relative singular spectrum entropy (LRSE), two-element multiscale entropy (TMSE), standard deviation (STD), RMS, and root-square amplitude (XR) are extracted and compose the original multidomain feature set. And then, locally preserving projection (LPP) is utilized to reduce dimension of original fusion feature set and genetic algorithm is applied to optimize the process of feature fusion. Finally, fuzzy recognition of rolling bearing degradation state is carried out by GG clustering and the principle of maximum membership degree and excellent performance of the proposed method is validated by comparing the recognition accuracy of LPP and GA-LPP.http://dx.doi.org/10.1155/2016/9281098 |
| spellingShingle | He Yu Hong-ru Li Zai-ke Tian Wei-guo Wang Rolling Bearing Degradation State Identification Based on LPP Optimized by GA International Journal of Rotating Machinery |
| title | Rolling Bearing Degradation State Identification Based on LPP Optimized by GA |
| title_full | Rolling Bearing Degradation State Identification Based on LPP Optimized by GA |
| title_fullStr | Rolling Bearing Degradation State Identification Based on LPP Optimized by GA |
| title_full_unstemmed | Rolling Bearing Degradation State Identification Based on LPP Optimized by GA |
| title_short | Rolling Bearing Degradation State Identification Based on LPP Optimized by GA |
| title_sort | rolling bearing degradation state identification based on lpp optimized by ga |
| url | http://dx.doi.org/10.1155/2016/9281098 |
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