@article{99d698f3-9b24-4a4a-b91d-d18f9ad8432d,
author = {Małgorzata Kuźnar},
title = {Prediction of thickness of pantograph contact strips using Artificial Neural Networks},
journal = {Technical Transactions},
volume = {2019},
number = {Volume 12 Year 2019 (116)},
year = {2019},
issn = {0011-4561},
pages = {173-180},keywords = {rail vehicles; pantograph; current collector; thickness prediction; sliding cover; artificial neural networks; ANN},
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.},
doi = {10.4467/2353737XCT.19.130.11455},
url = {https://ejournals.eu/en/journal/czasopismo-techniczne/article/prediction-of-thickness-of-pantograph-contact-strips-using-artificial-neural-networks}
}