Analysis of Dropper Stress in a Catenary System for a High-Speed Railway

Chen, Liming; Sun, Jun; Pan, Like; He, Fan

doi:10.1155/2022/9663767

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…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

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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.

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Section: Introductionmentioning

confidence: 99%

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

Rao

Liu

et al. 2023

Applied Sciences

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Numerical analysis of dropper stress under a moving load based on the uplift displacement for a high-speed railway

Yang,

Shen,

Pan

et al. 2024

MATH

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<abstract> <p>The most essential cause of the fracture of the dropper is the effect of alternating stress for a long time. Therefore, in order to ensure the safe operation of high-speed railways, the influence of moving loads on the stress of a dropper was investigated in this study. Due to a high-voltage catenary system, it is very difficult to measure the moving load. Thus, the uplift displacement measured by some software and hardware devices has been applied to the contact wire instead of the moving load. The response equation for the contact wire has been derived so as to determine the initial and boundary conditions of each dropper. Then it was combined with the equation for vibration analysis of the dropper and the stress of each dropper was calculated by using the finite-difference method based on a written MATLAB program. The results show that the dropper stress, during a certain period goes through two stages of immediate rebound and bending compression when the uplift displacement is large. After the pantograph passes, the vibration of the dropper tends to be smooth; also, dropper stress variation with timecan be described by three stages: immediate rebound, vibration attenuation, and bending compression. In addition, the maximum tensile stress of dropper Ⅳ was the highest. It indicates that dropper Ⅳ was more prone to fracture than other droppers.</p> </abstract>

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Analysis of Dropper Stress in a Catenary System for a High-Speed Railway

Cited by 2 publications

References 17 publications

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

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

Numerical analysis of dropper stress under a moving load based on the uplift displacement for a high-speed railway

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