A GFT Centrality-Based Importance Measure for Traction System of High-Speed Train

Identification of critical components plays an important part in assessing the reliability of the traction system. In view of the existing problems in the importance measurement methods of components, this paper attempts to introduce an alternative importance measurement called Graph Fourier Transform centrality (GFT centrality) in order to estimate critical components of the traction system from the perspective of the fault propagation. First, the structure of the traction system is constructed as a topological network based on complex network theory. Second, according to the topological network and the influence of a fault propagation mechanism, GFT centrality measurement is proposed to identify important components of the traction system. The results of a case study demonstrate that the method proposed in this paper is effective.