A reasoning approach-based pattern graph for analyzing the risk level of correlations among catenary components considering time distribution

•We define two types of fault correlations based on the time intervals among faulty components: simultaneous fault correlations and sequential fault correlations to investigate the fault propagation characteristics among faulty components at different time scales•The MYCIN method is introduced to construct the belief and disbelief models by defining the residual belief of fault correlations to evaluate the risk levels of fault correlations.•Simultaneous/sequential risk pattern graphs are proposed by linking and identifying virtual paths using explicit fault correlations to make reasonable inferences for inexplicit correlations that are included in the dataset but are hidden and thus not directly observable. The catenary system is a crucial part of the traction power supply system, consisting of multiple components interconnected through mechanical coupling. To reveal the risk characteristics of fault propagation between catenary components, this paper presents a reasoning approach-based pattern graph for analyzing the risk level of correlations of components considering time distribution from a statistical perspective. Initially, we define simultaneous fault correlations and sequential fault correlations among faulty components based on the different time distributions to capture the risk propagation features among components. Then, the MYCIN model is introduced to construct a certainty factor considering the belief and disbelief of fault correlations to calculate the risk levels of simultaneous/sequential fault correlations. Finally, we develop a risk pattern graph by linking the virtual paths to assess the risk level of inexplicit correlations hidden within the historical dataset. Simulation results, conducted based on the fault database of the Chengdu Railway Bureau, show the proposed method can effectively assess the risk level of correlations among faulty components to reveal the fault propagation features, which provides valuable references for proactive maintenance.