1. bookVolume 116 (2019): Issue 12 (December 2019)
Journal Details
License
Format
Journal
eISSN
2353-737X
First Published
20 May 2020
Publication timeframe
1 time per year
Languages
English
Open Access

Prediction of thickness of pantograph contact strips using Artificial Neural Networks

Published Online: 16 May 2020
Volume & Issue: Volume 116 (2019) - Issue 12 (December 2019)
Page range: 173 - 180
Received: 11 Dec 2019
Journal Details
License
Format
Journal
eISSN
2353-737X
First Published
20 May 2020
Publication timeframe
1 time per year
Languages
English
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.

Keywords

[1] EC Engeenering, Pantograf 160EC,http://www.ec-engineering.pl/produkcja/Zakład_Produkcyjny-Kraków/Pantograf_kolejowySearch in Google Scholar

[2] Abdullah M., Michitsuji Y., Nagai M., Integrated simulation between flexible body of catenary and active control pantograph for contact force variation control, Journal of Mechanical, 2010.10.1299/jmtl.3.166Search in Google Scholar

[3] Abdullah M., Michitsuji Y., Nagai M., Analysis of contact force variation between contact wire and pantograph based on multibody dynamics, Journal of Mechanical, 2010.10.1299/jmtl.3.552Search in Google Scholar

[4] Aboshi M., Precise measurement and estimation method for overhead contact line unevenness, IEEJ Transactions on Industry Applications, 2004.10.1541/ieejias.124.871Search in Google Scholar

[5] Aboshi M., Manabe K., Analyses of contact force fluctuation between catenary and pantograph, Quarterly Report of RTRI, 2000.10.2219/rtriqr.41.182Search in Google Scholar

[6] Allotta B., Pugi L., Bartolini F., An active suspension system for railway pantographs: the T2006 prototype, Engineers, Part F: Journal of Rail, 2009.10.1243/09544097JRRT174Search in Google Scholar

[7] Allotta B., Pugi L., Rindi A., Papi M., Innovative solutions for active railway pantograph, WIT Transactions, 2002.Search in Google Scholar

[8] Chater E., Ghani D., Giri F., Haloua M., Output feedback control of pantograph–catenary system with adaptive estimation of catenary parameters, Journal of Modern Transportation, 2015.10.1007/s40534-015-0085-zSearch in Google Scholar

[9] Gostling R.J., Hobbs A.E.W., The interaction of pantograph and overhead equipment: practical applications of a new theoretical method, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 197, no. 1, 1983, 61–69.10.1243/PIME_PROC_1983_197_077_02Search in Google Scholar

[10] Judek S., Karwowski K., Mizan M., Diagnostyka i monitoring odbioru prądu z sieci trakcyjnej, Pojazdy Szynowe, 2011.10.53502/RAIL-139554Search in Google Scholar

[11] Stichel S., Active Control of the Pantograph-Catenary Interaction in a Finite Element Model, 2013.Search in Google Scholar

[12] Sitarz M., Adamiec A., Mańka A., Uszkodzenia węglowych nakładek stykowych pantografów kolejowych stosowanych w Polsce, TTS Technika Transportu Szynowego, 2016.Search in Google Scholar

[13] Majewski W., Zastosowania nakładek węglowych w odbierakach prądu, Prezentacja Instytutu Kolejnictwa.Search in Google Scholar

[14] Rusiecki A., Algorytmy uczenia sieci neuronowych odporne na błędy w danych, Wrocław 2007.Search in Google Scholar

[15] Hagan M.T., Menhaj M.B., Training feedforward networks with the Marquardt algorithm, IEEE transactions on Neural Networks, vol. 5, no. 6, 1994, 989–993.10.1109/72.32969718267874Search in Google Scholar

[16] Marquardt D.W., An algorithm for least-squares estimation of nonlinear parameters, Journal of the society for Industrial and Applied Mathematics, vol. 11, no. 2, 1963, 431–441.10.1137/0111030Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo