Modelowanie i symulacja dynamiki ruchu trakcyjnego odbieraka prądu

Wilk, Andrzej; Karwowski, Krzysztof; Judek, Sławomir; Mizan, Mirosław

doi:10.15199/48.2016.10.55

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…One of the first papers presenting the basic mathematical model of contact between sliding strips and overhead contact line was published by Abbot [ 16 ] in 1970. A more detailed model was presented in 2015 by Wilk et al [ 17 , 18 ]. Abdullach et al [ 6 , 7 ] paid attention in their research to the integration of two separated models: the catenary model and the pantograph model.…”

Section: Introductionmentioning

confidence: 99%

“…Tao et al [ 20 ] and Ding et al [ 21 ] researched tribology behaviours of a carbon strip and copper wire under electric currents. Many of the presented studies work mainly on the mathematical methods of simulation of dynamic [ 16 , 17 , 18 , 22 , 23 , 24 , 25 ] and analysis of contact force [ 6 , 7 , 9 , 19 , 26 , 27 , 28 , 29 ], as well as on the wear of sliding strip material [ 20 , 21 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. The papers focus on material properties and the interaction between sliding strips and catenary.…”

Section: Introductionmentioning

confidence: 99%

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A Method of Predicting Wear and Damage of Pantograph Sliding Strips Based on Artificial Neural Networks

Kuźnar

Lorenc

2021

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The impact of the pantograph of a rail vehicle on the overhead contact line depends on many factors. Among other things, the type of pantograph, i.e., the material of the sliding strip, influences the wear and possible damage to the sliding strip. The possibility of predicting pantograph failures may make it possible to reduce the number of these kinds of failures. This article presents a method for predicting the technical state of the pantograph by using artificial neural networks. The presented method enables the prediction of the wear and damage of the pantograph, with particular emphasis on carbon sliding strips. The paper compares 12 predictive models based on regression algorithms, where different training algorithms and activation functions were used. Two different types of training data were also used. Such a distinction made it possible to determine the optimal structure of the input and output data teaching the neural network, as well as the determination of the best structure and parameters of the model enabling the prediction of the technical condition of the current collector.

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