Abstract
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.
Highlights
Maglev train is a new kind of railway transportation tool which features in the contact-less interact between train body and the track [1]
To meet different application requirements, there has been a variety of maglev trains, among which permanent magnet electromagnetic suspension (PEMS) type maglev train is an innovative maglev aiming at energy-saving and long time levitation [4]
In this paper, a data-driven state monitoring problem for PEMS high speed maglev train is studied in detail
Summary
Maglev train is a new kind of railway transportation tool which features in the contact-less interact between train body and the track [1]. To meet different application requirements, there has been a variety of maglev trains, among which permanent magnet electromagnetic suspension (PEMS) type maglev train is an innovative maglev aiming at energy-saving and long time levitation [4]. For commercial operational maglev train, the performances of levitation system degrades as time goes by. This degradation can be caused by the wearing and the aging of electrical components. The second case is the demagnetization of permanent magnet Both cases will cause the diminishing of magnetic force generated by the hybrid electromagnet, and further results in degradation of system performance. The ndimensional Euclidean space is denoted as Rn, the set of all m × n real matrix are denoted as Rm×n, the superscript "T " stands for the transpose of a matrix, the superscript "−1" is used for describing the inverse of a matrix
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