<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The sliding strip of the current collector (pantograph) of a rail vehicle is an element directly cooperating with the catenary and is exposed to abrasion, electric discharge and various types of damage. It is therefore the most frequently replaced element. However, often sliding strips are exchanged before exceeding the limit thickness value, which increases the costs related to technical maintenance. Because the wear process is dependent on many factors, heuristic methods are necessary to predict the thickness of the sliding strip. Knowing the predicted thickness value, it will be possible to adapt the maintenance cycle. In the article, the results of simulations carried out based on the developed structure of the artificial neural network are also presented.</p></abstract>

The sliding strip of the current collector (pantograph) of a rail vehicle is an element directly cooperating with the catenary and is exposed to abrasion, electric discharge and various types of damage. It is therefore the most frequently replaced element. However, often sliding strips are exchanged before exceeding the limit thickness value, which increases the costs related to technical maintenance. Because the wear process is dependent on many factors, heuristic methods are necessary to predict the thickness of the sliding strip. Knowing the predicted thickness value, it will be possible to adapt the maintenance cycle. In the article, the results of simulations carried out based on the developed structure of the artificial neural network are also presented.

Prediction of thickness of pantograph contact strips using Artificial Neural Networks

Reliability and Technical Exploitation Division

Technical Transactions

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 Public License.

The sliding strip of the current collector (pantograph) of a rail vehicle is an element directly cooperating with the catenary and is exposed to abrasion,...

Prediction of thickness of pantograph contact strips using Artificial Neural Networks

Data publikacji: 16 maj 2020

Zakres stron: 173 - 180

Otrzymano: 11 gru 2019

DOI: https://doi.org/10.4467/2353737XCT.19.130.11455

Słowa kluczowe
rail vehicles, pantograph, current collector, thickness prediction, sliding cover, artificial neural networks, ANN

© 2019 Małgorzata Kuźnar, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 Public License.

Prediction of thickness of pantograph contact strips using Artificial Neural Networks

Data publikacji: 16 maj 2020

Zakres stron: 173 - 180

Otrzymano: 11 gru 2019

DOI: https://doi.org/10.4467/2353737XCT.19.130.11455

Słowa kluczowerail vehicles, pantograph, current collector, thickness prediction, sliding cover, artificial neural networks, ANN

© 2019 Małgorzata Kuźnar, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 Public License.

Słowa kluczowe
rail vehicles, pantograph, current collector, thickness prediction, sliding cover, artificial neural networks, ANN