Dynamic behavior analysis of touchdown process in active magnetic bearing system based on a machine learning method

Zhe Sun*, Xunshi Yan, Jingjing Zhao, Xiao Kang, Guojun Yang, Zhengang Shi

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

8 引用 (Scopus)

摘要

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.

源语言英语
文章编号1839871
期刊Science and Technology of Nuclear Installations
2017
DOI
出版状态已出版 - 2017
已对外发布

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