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

Abstract

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 paper, 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. INDEX TERMS Fault detection; auto-correlation length; random matrix theory; mean spectral radius; suspension system.