A Novel Arcing Detection Model of Pantograph–Catenary for High-Speed Train in Complex Scenes

Liu, Yang; Quan, Wei; Lu, Xuemin; Liu, Xiaohong; Gao, Shibin; Zhao, Haiquan; Yu, Long; Jie, Zheng

doi:10.1109/tim.2023.3267365

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Kang et al [8,9] proposed a contact wire support and insulator defect detection system capable of localizing the key components of a catenary and evaluating its defect status using deep Bayesian segmentation neural networks and prior geometric knowledge. Liu Y et al [10] introduced a robust 2 of 18 image-based semantic segmentation model named ArcMSF for pantograph catenary arcing detection. While the visual-based detection method is effective in quickly identifying faulty components, it is limited to visible faults and lacks the ability to continuously monitor the status of the catenary in real time.…”

Section: Introductionmentioning

confidence: 99%

Defect Diagnosis of Rigid Catenary System Based on Pantograph Vibration Performance

Tan,

Li,

Nie

2024

Actuators

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The condition of a catenary is significant to ensure a high current collection quality. Owing to the dynamic interaction between the pantograph and the catenary system, the vibration of the pantograph can be used to analyze the condition of the catenary system. Therefore, we developed a novel diagnosis system based on the correlation between catenary defects and pantograph vibration. The proposed system is capable of detecting the type and location of commonly encountered defects in rigid support catenary systems. Catenary positioning coefficient and enhanced sample entropy methods were proposed for the extraction of defect features, and subsequently, linear discriminate analysis was used to diagnose the type and location of the catenary defects. Finally, the proposed defect detection and diagnosis system was applied to a commercial metro line, and the results verified the reliability and effectiveness of this diagnosis system.

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Section: Introductionmentioning

confidence: 99%