Assessment of catenary condition monitoring by means of pantograph head acceleration and Artificial Neural Networks

Gregori, S.; Tur, M.; Gil, J.; Fuenmayor, F.J.

doi:10.1016/j.ymssp.2023.110697

Cited by 6 publications

(1 citation statement)

References 35 publications

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“…Bocciolone M et al [15] and Carnevale M et al [16] detected defects by observing the occurrence of high peaks in the measured contact force and vertical acceleration values. S. Gregori [17] used a simulation model to generate acceleration data of pantograph heads under fault conditions and used it to train a neural network model, achieving state assessment of the contact network and diagnosis of contact wire wear and irregularities. The condition monitoring of a catenary can realize the abnormal state detection of a rigid catenary; however, the diagnosis of defects remains a challenge and has not been effectively resolved as defect identification and separation are quite difficult.…”

Section: Introductionmentioning

confidence: 99%

Defect Diagnosis of Rigid Catenary System Based on Pantograph Vibration Performance

Tan,

Li,

Nie

2024

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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%