Intelligent fault diagnosis of railway pantograph using a novel graph construction methodology

Abstract

Railway pantographs provide power for railway vehicles by conducting electrical energy from overhead catenary. The failure of the pantograph tends to damage the contact quality between the pantograph and the catenary, reducing the transmission efficiency of electric energy. Hence, fault diagnosis of pantograph plays a significant role in expanding the service life of railway vehicles. In this work, a novel graph construction method is proposed for the fault diagnosis of pantographs combined with a graph Neural Network (GNN). In the graph construction method, 1D load signals collected from the test pantograph are firstly transformed into multiple 2D images with the same size in both time and frequency domains using Gramian Angular Field (GAF), Markov Transition Field (MTF) and Recurrence Plot (RP). Secondly, Pixel values in images are regarded as features in vertexes of graphs, and graphs can be constructed by connecting eight neighbor vertexes. Finally, the GNN model is trained by constructed graphs for obtaining the fault diagnosis model of pantographs. Laboratory experiments are implemented to show the advantages of the proposed method by comparing it with other conventional methods. There are two main innovations of this approach: 1. Handling the data with different lengths. 2. Performing perfectly in the situation of small training data size.