Fault Detection Method for Suspension Systems of Maglev Train Based on Optimized Random Matrix Theory

IEEE Access

Jiewei Zeng¹  Ping Wang¹  Xu Zhou¹  Fu Feng²  Hailin Hu²  Gao Wang³ 

[1] College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China;School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China;School of Physics and Astronomy, University of Glasgow, Glasgow, U.K.;

关键词: Fault detection;    auto-correlation length;    random matrix theory;    mean spectral radius;    suspension system;   

DOI  :  10.1109/ACCESS.2020.3024777

来源: DOAJ

【 摘 要 】

The fault detection of the suspension system in a maglev train is of great importance for its operational safety and reliability. However, in random matrix theory (RMT), the size of the random matrix direct impacts the result of the mean spectral radius (MSR). In this article, a state-of-the-art fault detection method for suspension systems is proposed using optimized RMT. The random matrix with the largest number of eigenvalues is obtained by reshaping the original data, with the help of the auto-correlation length from the correlation analysis. Finally, the optimized MSR is applied to detect the fault. The results of the experiment illustrate that the proposed method is applicable and effective.

【 授权许可】

Unknown