An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics
Aimed at degradation prognostics of a rolling bearing, this paper proposed a novel cumulative transformation algorithm for data processing and a feature fusion technique for bearing degradation assessment. First, a cumulative transformation is presented to map the original features extracted from a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/9067184 |
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| _version_ | 1832551493030379520 |
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| author | Lixiang Duan Fei Zhao Jinjiang Wang Ning Wang Jiwang Zhang |
| author_facet | Lixiang Duan Fei Zhao Jinjiang Wang Ning Wang Jiwang Zhang |
| author_sort | Lixiang Duan |
| collection | DOAJ |
| description | Aimed at degradation prognostics of a rolling bearing, this paper proposed a novel cumulative transformation algorithm for data processing and a feature fusion technique for bearing degradation assessment. First, a cumulative transformation is presented to map the original features extracted from a vibration signal to their respective cumulative forms. The technique not only makes the extracted features show a monotonic trend but also reduces the fluctuation; such properties are more propitious to reflect the bearing degradation trend. Then, a new degradation index system is constructed, which fuses multidimensional cumulative features by kernel principal component analysis (KPCA). Finally, an extreme learning machine model based on phase space reconstruction is proposed to predict the degradation trend. The model performance is experimentally validated with a whole-life experiment of a rolling bearing. The results prove that the proposed method reflects the bearing degradation process clearly and achieves a good balance between model accuracy and complexity. |
| format | Article |
| id | doaj-art-d43c4dae81fc4643975a217c7b236073 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-d43c4dae81fc4643975a217c7b2360732025-02-03T06:01:23ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/90671849067184An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation PrognosticsLixiang Duan0Fei Zhao1Jinjiang Wang2Ning Wang3Jiwang Zhang4School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaSichuan Special Equipment Inspection and Research Institute, Chengdu 610051, ChinaSchool of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaSchool of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaSchool of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaAimed at degradation prognostics of a rolling bearing, this paper proposed a novel cumulative transformation algorithm for data processing and a feature fusion technique for bearing degradation assessment. First, a cumulative transformation is presented to map the original features extracted from a vibration signal to their respective cumulative forms. The technique not only makes the extracted features show a monotonic trend but also reduces the fluctuation; such properties are more propitious to reflect the bearing degradation trend. Then, a new degradation index system is constructed, which fuses multidimensional cumulative features by kernel principal component analysis (KPCA). Finally, an extreme learning machine model based on phase space reconstruction is proposed to predict the degradation trend. The model performance is experimentally validated with a whole-life experiment of a rolling bearing. The results prove that the proposed method reflects the bearing degradation process clearly and achieves a good balance between model accuracy and complexity.http://dx.doi.org/10.1155/2018/9067184 |
| spellingShingle | Lixiang Duan Fei Zhao Jinjiang Wang Ning Wang Jiwang Zhang An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics Shock and Vibration |
| title | An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics |
| title_full | An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics |
| title_fullStr | An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics |
| title_full_unstemmed | An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics |
| title_short | An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics |
| title_sort | integrated cumulative transformation and feature fusion approach for bearing degradation prognostics |
| url | http://dx.doi.org/10.1155/2018/9067184 |
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