The influence of wind load on the stress characteristics of dropper for a high-speed railway

Chen, Liming; Guo, Dan; Pan, Like; He, Fan

doi:10.1177/16878132221097833

Cited by 1 publication

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, research on catenary droppers tends to focus on engineering structures and geometric parameters, as well as the impact of the suspension string design on train operation safety at the physical level. For example, the inrush current characteristics [10], stress characteristics [11,12], dynamic performance [13,14], geometric characteristics [15], wind resistance [16], and fatigue analysis [17] of the droppers have recently been studied. To date, there has been relatively little research on the intelligent detection of dropper faults.…”

Section: Introductionmentioning

confidence: 99%

YOLOv5s-D: A Railway Catenary Dropper State Identification and Small Defect Detection Model

Rao

Liu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

High-speed railway catenaries are vital components in railway traction power supply systems. To ensure stable contact between the pantograph and the catenary, droppers are positioned between the messenger wire and contact line. The failure of one or more droppers will affect the power supply of the catenary and the operation of the railway. In this paper, we modify the You Only Look Once version five (YOLOv5) model in several ways and propose a method for improving the identification of dropper status and the detection of small defects. Firstly, to focus on small target features, the selective kernel attention module is added to the backbone. Secondly, the feature graphs of different scales extracted from the backbone network are fed into the bidirectional feature pyramid network for multiscale feature fusion. Thirdly, the YOLO head is replaced by a decoupled head to improve the convergence speed and detection accuracy of the model. The experimental results show that the proposed model achieves a mean average precision of 92.9% on the dropper dataset, an increase of 3.8% over the results using YOLOv5s. The detection accuracy of small dropper defects reaches 79.2%, representing an increase of 10.8% compared with YOLOv5s and demonstrating that our model is better at detecting small defects.

show abstract