Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning Method
Magnetic bearings are widely applied in High Temperature Gas-cooled Reactor (HTGR) and auxiliary bearings are important backup and safety components in AMB systems. The performance of auxiliary bearings significantly affects the reliability, safety, and serviceability of the AMB system, the rotating...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2017/1839871 |
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| _version_ | 1832560904853520384 |
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| author | Zhe Sun Xunshi Yan Jingjing Zhao Xiao Kang Guojun Yang Zhengang Shi |
| author_facet | Zhe Sun Xunshi Yan Jingjing Zhao Xiao Kang Guojun Yang Zhengang Shi |
| author_sort | Zhe Sun |
| collection | DOAJ |
| description | Magnetic bearings are widely applied in High Temperature Gas-cooled Reactor (HTGR) and auxiliary bearings are important backup and safety components in AMB systems. The performance of auxiliary bearings significantly affects the reliability, safety, and serviceability of the AMB system, the rotating equipment, and the whole reactor. Research on the dynamic behavior during the touchdown process is crucial for analyzing the severity of the touchdown. In this paper, a data-based dynamic analysis method of the touchdown process is proposed. The dynamic model of the touchdown process is firstly established. In this model, some specific mechanical parameters are regarded as functions of deformation of auxiliary bearing and velocity of rotor firstly; furthermore, a machine learning method is utilized to model these function relationships. Based on the dynamic model and the Kalman filtering technique, the proposed method can offer estimation of the rotor motion state from noisy observations. In addition, the estimation precision is significantly improved compared with the method without learning. The proposed method is validated by the experimental data from touchdown experiments. |
| format | Article |
| id | doaj-art-d7e4591496894d1696a98fb9c4aa9e22 |
| institution | Kabale University |
| issn | 1687-6075 1687-6083 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Science and Technology of Nuclear Installations |
| spelling | doaj-art-d7e4591496894d1696a98fb9c4aa9e222025-02-03T01:26:33ZengWileyScience and Technology of Nuclear Installations1687-60751687-60832017-01-01201710.1155/2017/18398711839871Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning MethodZhe Sun0Xunshi Yan1Jingjing Zhao2Xiao Kang3Guojun Yang4Zhengang Shi5Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, ChinaInstitute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, ChinaInstitute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, ChinaMechanical Engineering, Texas A&M University, College Station, TX 77843, USAInstitute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, ChinaInstitute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, ChinaMagnetic bearings are widely applied in High Temperature Gas-cooled Reactor (HTGR) and auxiliary bearings are important backup and safety components in AMB systems. The performance of auxiliary bearings significantly affects the reliability, safety, and serviceability of the AMB system, the rotating equipment, and the whole reactor. Research on the dynamic behavior during the touchdown process is crucial for analyzing the severity of the touchdown. In this paper, a data-based dynamic analysis method of the touchdown process is proposed. The dynamic model of the touchdown process is firstly established. In this model, some specific mechanical parameters are regarded as functions of deformation of auxiliary bearing and velocity of rotor firstly; furthermore, a machine learning method is utilized to model these function relationships. Based on the dynamic model and the Kalman filtering technique, the proposed method can offer estimation of the rotor motion state from noisy observations. In addition, the estimation precision is significantly improved compared with the method without learning. The proposed method is validated by the experimental data from touchdown experiments.http://dx.doi.org/10.1155/2017/1839871 |
| spellingShingle | Zhe Sun Xunshi Yan Jingjing Zhao Xiao Kang Guojun Yang Zhengang Shi Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning Method Science and Technology of Nuclear Installations |
| title | Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning Method |
| title_full | Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning Method |
| title_fullStr | Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning Method |
| title_full_unstemmed | Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning Method |
| title_short | Dynamic Behavior Analysis of Touchdown Process in Active Magnetic Bearing System Based on a Machine Learning Method |
| title_sort | dynamic behavior analysis of touchdown process in active magnetic bearing system based on a machine learning method |
| url | http://dx.doi.org/10.1155/2017/1839871 |
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