Data Driven State Monitoring of Maglev System With Experimental Analysis

被引：3
作者：
Wang, Zhiqiang [1 ,2 ]
Long, Zhiqiang [1 ,2 ]
Luo, Jie [1 ]
He, Zhangming [3 ]
Li, Xiaolong [1 ]
机构：
[1] Natl Univ Def Technol, Maglev Engn Res Ctr, Changsha 410073, Peoples R China
[2] Hunan Prov Key Lab Electromagnet Levitat & Prop T, Changsha 410073, Peoples R China
[3] Natl Univ Def Technol, Coll Liberal Arts & Sci, Changsha 410073, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷
关键词：
Levitation; Electromagnets; Monitoring; Permanent magnets; Springs; Magnetic forces; Electromagnetics; High speed Maglev train; PEMS; Levitation system; data-driven; state monitoring; FAULT-TOLERANT CONTROL; OPTIMIZATION;
D O I：
10.1109/ACCESS.2020.2988772
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The reliability of levitation system plays an important role for the safe operation of maglev train. Monitoring the state of the levitation system helps make early judgement to adopt fault tolerant measurement preventing further damage. In this paper, a data-driven state monitoring problem for PEMS high speed maglev train is studied in detail. Firstly preliminaries about levitation system and problem formulation are described. Then a residual generation method based on system input/ouptput data is given. To tackle the varying operational condition problem, a multi-model switching strategy is proposed. For the non-Gaussian property of the system data, a Box-Cox transformation is adopted. The effectiveness of the proposed method is illustrated by experimental data analysis results.
引用
收藏
页码：79104 / 79113
页数：10
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