Pantograph Spark Fault Detection using YOLO

Amenya Bright ,Jingzhong Ding

doi:10.7176/isde/12-3-02

Abstract

Pantograph-catenary is now the dominant form of current collection for modern electric trains because they can be used for higher voltages. Faults in pantograph-catenary systems threaten the operation and safety of railway transportation. They need to be continuously monitored and controlled to maintain safe transport. Pantograph may be damaged as a result of extreme weather conditions which can affect its normal operation, leading to failure of pantograph and overhead contact line systems. Poor contact between pantograph and overhead contact line causes thermal erosion to the wire. When the pantographs are exposed to air, they could deteriorate due to electrochemical reaction with the environment since they are made of metals. Movement of catenary lines and pantograph in high crosswinds has been found to cause the wire to be trapped in the pantograph. There is a serious issue regarding the quality of images generated by pantograph video monitoring system on high-speed railway trains which often shows inconsistencies of catenary faults. The application of traditional image processing and deep learning techniques have been unable to meet the requirements of spark detection. In this paper, a modern deep learning algorithm is proposed to detect sparks in the pantograph. Specifically, the YOLOv3 model is used to counter this problem that traditional image processing algorithms have been unable to. The results on a very large sample of data show the efficiency and real-time performance of the proposed method, which meets the requirements of pantograph spark detection in high-speed railway. Keywords : High-speed railway pantograph; Spark detection; Deep learning; YOLOv3; DOI: 10.7176/ISDE/12-3-02 Publication date: September 30 th 2021

Highlights

1.0 Introduction Catenary is an important part of the traction power supply system in high speed railways and it consists of some catenary support components such as insulator, rotary double ear, and brace sleeve
The use of a YOLO network is recommended in this research
4.0 CONCLUSION The pantograph detection model based on deep learning YOLOv3 was used to locate the pantograph parts in this paper, and the spark location between the catenary and the pantograph and the characteristics of the spark image were analyzed

Summary

Introduction

Catenary is an important part of the traction power supply system in high speed railways and it consists of some catenary support components such as insulator, rotary double ear, and brace sleeve. The normal state of catenary support components is the basis of the normal operation of high speed trains. Fault states of catenary support components could occur due to the vibration caused by vehicles and the complex natural environment near the railway. Pantograph condition monitoring has the ability to increase pantograph performance while lowering maintenance costs. A laboratory-based pantograph test rig has been constructed to acquire a knowledge of pantograph dynamic behaviours, when incipient defects are present, as a first step in realizing practical pantograph condition monitoring (Xin, et al, 2020)

Methods

Conclusion