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

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2017
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Ranked #15 of 65 articles by views in drug metabolism reviews

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Abstract
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.
Reference Key
sun2017sciencedynamic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Zhe Sun;Xunshi Yan;Jingjing Zhao;Xiao Kang;Guojun Yang;Zhengang Shi
Journal drug metabolism reviews
Year 2017
DOI
10.1155/2017/1839871
URL
http://dx.doi.org/10.1155/2017/1839871
Keywords
electrical engineering electronics nuclear engineering

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