A Deep Learning Based Method for Detecting of Wear on the Current Collector Strips’ Surfaces of the Pantograph in Railways

Gulsah Karaduman,Erhan Akin

doi:10.1109/access.2020.3029555

Gulsah Karaduman, Erhan Akin

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https://doi.org/10.1109/access.2020.3029555

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 21	License type: CC BY 4.0

Affiliation: Fırat University

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In pantographs, current collector strips transmit the electrical energy they receive from the catenary to the locomotive and provide the necessary power for the locomotive's movement. In order for the current collector strips to transmit electricity to the locomotive in a healthy way, their surface must be smooth. Wear on the surface of the current collector strips reduces conductivity and can create arcs, endangering the health and safety of the pantograph and catenary system. In this paper, a Convolutional Neural Network (CNN) architecture is developed to detect wear on the current collector strips. Images obtained from pantographs used on railways were created with a clean and improved data set using Hough Transform and Power Law Transform. The created dataset contains 909 pantograph images. This dataset was trained and tested with both the developed CNN architecture and classic deep learning architectures (ResNet50, VGG16). The experimental results show that the developed CNN architecture's training results and test results are more successful than classical architectures.

Highlights

Rail systems constitute the backbone of the economy throughout the world, especially considering its freight transportation potential
EXPERIMENTAL RESULTS Lines were obtained by applying Canny Edge Detection and Hough Transform to the current collector strips images obtained from pantographs in the Turkey Republic State Railways
This study was carried out to determine the wear on the current collection strips of the pantograph using images of pantographs containing wear obtained from the railways and belonging to four wear classes

Summary

Introduction

Rail systems constitute the backbone of the economy throughout the world, especially considering its freight transportation potential. A special Convolutional Neural Network (CNN) architecture was developed to increase the detection success of the wear in the pantograph’s current collecting strips. THE PROPOSED MODEL In this study, a method is proposed for detecting wear in the current collector strips of the pantograph.

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