Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System
Bearing is one of the most critical mechanical components in rotating machinery. To identify the running status of bearing effectively, a variety of possible fault vibration signals are recorded under multiple speeds. However, the acquired vibration signals have different characteristics under diffe...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6627305 |
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| _version_ | 1849434999358488576 |
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| author | Tingting Wu Yufen Zhuang Bi Fan Hainan Guo Wei Fan Cai Yi Kangkang Xu |
| author_facet | Tingting Wu Yufen Zhuang Bi Fan Hainan Guo Wei Fan Cai Yi Kangkang Xu |
| author_sort | Tingting Wu |
| collection | DOAJ |
| description | Bearing is one of the most critical mechanical components in rotating machinery. To identify the running status of bearing effectively, a variety of possible fault vibration signals are recorded under multiple speeds. However, the acquired vibration signals have different characteristics under different speeds and environment interference, which may lead to different diagnosis results. In order to improve the fault diagnosis reliability, a multidomain feature fusion for varying speed bearing diagnosis using broad learning system is proposed. First, a multidomain feature fusion is adopted to realize the unified form of vibration characteristics at different speeds. Time-domain and frequency-domain features are extracted from the different speeds vibration signals. Then, the broad learning system is employed with the fused features for classification. Our experimental results suggest that, compared with other machine learning models, the proposed broad learning system model, which makes full use of the fused feature, can improve the diagnosis performance significantly in terms of both accuracy and robustness analysis. |
| format | Article |
| id | doaj-art-e49aa05ad40e467d8797705ab06ff41b |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-e49aa05ad40e467d8797705ab06ff41b2025-08-20T03:26:26ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66273056627305Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning SystemTingting Wu0Yufen Zhuang1Bi Fan2Hainan Guo3Wei Fan4Cai Yi5Kangkang Xu6College of Management, Shenzhen University, Shenzhen 518061, ChinaCollege of Management, Shenzhen University, Shenzhen 518061, ChinaCollege of Management, Shenzhen University, Shenzhen 518061, ChinaCollege of Management, Shenzhen University, Shenzhen 518061, ChinaSchool of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaBearing is one of the most critical mechanical components in rotating machinery. To identify the running status of bearing effectively, a variety of possible fault vibration signals are recorded under multiple speeds. However, the acquired vibration signals have different characteristics under different speeds and environment interference, which may lead to different diagnosis results. In order to improve the fault diagnosis reliability, a multidomain feature fusion for varying speed bearing diagnosis using broad learning system is proposed. First, a multidomain feature fusion is adopted to realize the unified form of vibration characteristics at different speeds. Time-domain and frequency-domain features are extracted from the different speeds vibration signals. Then, the broad learning system is employed with the fused features for classification. Our experimental results suggest that, compared with other machine learning models, the proposed broad learning system model, which makes full use of the fused feature, can improve the diagnosis performance significantly in terms of both accuracy and robustness analysis.http://dx.doi.org/10.1155/2021/6627305 |
| spellingShingle | Tingting Wu Yufen Zhuang Bi Fan Hainan Guo Wei Fan Cai Yi Kangkang Xu Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System Shock and Vibration |
| title | Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System |
| title_full | Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System |
| title_fullStr | Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System |
| title_full_unstemmed | Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System |
| title_short | Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System |
| title_sort | multidomain feature fusion for varying speed bearing diagnosis using broad learning system |
| url | http://dx.doi.org/10.1155/2021/6627305 |
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