A Novel Method Based on Deep Learning and Image Processing Techniques for Wearing Inspection on the Pantograph Surface

Abstract

Wear is an important problem in the pantograph-catenary system of electric rail transportation vehicles, which are increasingly used in the world, which receive their energy from the overhead line. This problem causes an increase in unplanned repair costs and disruption of transportation. The pantograph current collector strips move the locomotive with the electrical energy they receive from the catenary. Wear failure on the surface of the current collector strips creates problems in the pantograph-catenary interaction, causing transportation disruption and the safety of transportation is significantly affected. In this study, wear on the current collector strip surface has been detected by the noncontact monitoring method. A Convolution Neural Network (CNN) architecture has been developed to diagnose wear. Before the current collector strip surface images are trained on the CNN architecture, it is aimed to clarify the wear images and to make the diagnosis correctly with some pre-processing techniques on the images. After the pre-processing steps applied to the images, the performance results regarding the training of the dataset and its training with the data with the raw data have been compared, and more successful results have been obtained from the model trained using the pre-processed dataset. © 2021 IEEE.