01748624

Component

https://doi.org/10.1177/0361198120937964

The pantograph-catenary system is critical to high-speed railways. Electric arcs in the pantograph-catenary system indicate possible damages to the whole railway system, and detecting them in time has been a critical task. In this paper, a fusion method for the pantograph-catenary arc detection based on multi-type videos is proposed. First, convolutional neural network (CNN) is employed to detect arcs in visible light images, and a threshold method is applied to identify arcs in infrared images. Second, the CNN-based environment perception model is established on visible light images. It obtains the dynamical adjustment of the reliability weights for different scenarios where trains usually work. Finally, the information fusion model based on evidence theory uses those weights and integrates the detection results on visible light images and infrared results. The experimental results demonstrate the fusion method can avoid misjudgments of the two individual detection methods in certain scenarios, and perform better than each of them. This approach can adapt to the complex environments of high-speed trains.

The data used to support the findings of this study are available from the corresponding author on request. © National Academy of Sciences: Transportation Research Board 2020.

English

pp 229-240

2020-10

Transportation Research Record: Journal of the Transportation Research Board

Web

Figures; References (23)

Catenaries (Railroads); Detection and identification systems; Electric arcs; High speed rail; Neural networks; Pantographs; Video

Energy; Railroads; Vehicles and Equipment

TRIS, TRB, ATRI

Aug 22 2020 3:04PM