A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm
In this paper, a novel model for fault detection of rolling bearing is proposed. It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). The fundamental of multikernel least square support ve...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/9096852 |
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| _version_ | 1849690890472259584 |
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| author | Yerui Fan Chao Zhang Yu Xue Jianguo Wang Fengshou Gu |
| author_facet | Yerui Fan Chao Zhang Yu Xue Jianguo Wang Fengshou Gu |
| author_sort | Yerui Fan |
| collection | DOAJ |
| description | In this paper, a novel model for fault detection of rolling bearing is proposed. It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). The fundamental of multikernel least square support vector machine (MK-LS-SVM) is overviewed to identify a classifier that allows multidimension features from empirical mode decomposition (EMD) to be fused with high generalization property. Then the multidimension parameters of the MK-LS-SVM are configured by the SRPSO for further performance improvement. Finally, the proposed model is evaluated through experiments and comparative studies. The results prove its effectiveness in detecting and classifying bearing faults. |
| format | Article |
| id | doaj-art-ed7e770a0fff495391f578877dc556fc |
| institution | DOAJ |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-ed7e770a0fff495391f578877dc556fc2025-08-20T03:21:11ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/90968529096852A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle SwarmYerui Fan0Chao Zhang1Yu Xue2Jianguo Wang3Fengshou Gu4School of Mechanical Engineering, University of Science and Technology of the Inner Mongol, Baotou 014010, ChinaSchool of Mechanical Engineering, University of Science and Technology of the Inner Mongol, Baotou 014010, ChinaBeijing Tianrun New Energy Investment Co., Ltd., Beijing 100000, ChinaSchool of Mechanical Engineering, University of Science and Technology of the Inner Mongol, Baotou 014010, ChinaDepartment of Engineering and Technology, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKIn this paper, a novel model for fault detection of rolling bearing is proposed. It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). The fundamental of multikernel least square support vector machine (MK-LS-SVM) is overviewed to identify a classifier that allows multidimension features from empirical mode decomposition (EMD) to be fused with high generalization property. Then the multidimension parameters of the MK-LS-SVM are configured by the SRPSO for further performance improvement. Finally, the proposed model is evaluated through experiments and comparative studies. The results prove its effectiveness in detecting and classifying bearing faults.http://dx.doi.org/10.1155/2020/9096852 |
| spellingShingle | Yerui Fan Chao Zhang Yu Xue Jianguo Wang Fengshou Gu A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm Shock and Vibration |
| title | A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm |
| title_full | A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm |
| title_fullStr | A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm |
| title_full_unstemmed | A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm |
| title_short | A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm |
| title_sort | bearing fault diagnosis using a support vector machine optimised by the self regulating particle swarm |
| url | http://dx.doi.org/10.1155/2020/9096852 |
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