Data Driven State Monitoring of Maglev System With Experimental Analysis

Data Driven State Monitoring of Maglev System With Experimental Analysis

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...

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Main Authors: Zhiqiang Wang, Zhiqiang Long, Jie Luo, Zhangming He, Xiaolong Li
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
High speed Maglev train
PEMS
Levitation system
data-driven
state monitoring
Online Access:https://ieeexplore.ieee.org/document/9072168/
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spelling doaj-1ac05fb85534460d98af2820184ce5652021-03-30T01:33:44ZengIEEEIEEE Access2169-35362020-01-018791047911310.1109/ACCESS.2020.29887729072168Data Driven State Monitoring of Maglev System With Experimental AnalysisZhiqiang Wang0https://orcid.org/0000-0002-5094-0506Zhiqiang Long1Jie Luo2Zhangming He3https://orcid.org/0000-0001-9463-4327Xiaolong Li4Maglev Engineering Research Center, National University of Defense Technology, Changsha, ChinaMaglev Engineering Research Center, National University of Defense Technology, Changsha, ChinaMaglev Engineering Research Center, National University of Defense Technology, Changsha, ChinaCollege of Liberal Arts and Sciences, National University of Defense Technology, Changsha, ChinaMaglev Engineering Research Center, National University of Defense Technology, Changsha, ChinaThe 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.https://ieeexplore.ieee.org/document/9072168/High speed Maglev trainPEMSLevitation systemdata-drivenstate monitoring
collection DOAJ
language English
format Article
sources DOAJ
author Zhiqiang Wang
Zhiqiang Long
Jie Luo
Zhangming He
Xiaolong Li
spellingShingle Zhiqiang Wang
Zhiqiang Long
Jie Luo
Zhangming He
Xiaolong Li
Data Driven State Monitoring of Maglev System With Experimental Analysis
IEEE Access
High speed Maglev train
PEMS
Levitation system
data-driven
state monitoring
author_facet Zhiqiang Wang
Zhiqiang Long
Jie Luo
Zhangming He
Xiaolong Li
author_sort Zhiqiang Wang
title Data Driven State Monitoring of Maglev System With Experimental Analysis
title_short Data Driven State Monitoring of Maglev System With Experimental Analysis
title_full Data Driven State Monitoring of Maglev System With Experimental Analysis
title_fullStr Data Driven State Monitoring of Maglev System With Experimental Analysis
title_full_unstemmed Data Driven State Monitoring of Maglev System With Experimental Analysis
title_sort data driven state monitoring of maglev system with experimental analysis
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description 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.
topic High speed Maglev train
PEMS
Levitation system
data-driven
state monitoring
url https://ieeexplore.ieee.org/document/9072168/
work_keys_str_mv AT zhiqiangwang datadrivenstatemonitoringofmaglevsystemwithexperimentalanalysis
AT zhiqianglong datadrivenstatemonitoringofmaglevsystemwithexperimentalanalysis
AT jieluo datadrivenstatemonitoringofmaglevsystemwithexperimentalanalysis
AT zhangminghe datadrivenstatemonitoringofmaglevsystemwithexperimentalanalysis
AT xiaolongli datadrivenstatemonitoringofmaglevsystemwithexperimentalanalysis
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