International Journal of Advanced Studies (Mar 2023)

THE USE OF ARTIFICIAL NEURAL NETWORKS ON RUSSIAN RAILWAYS TO CONTROL CURRENT COLLECTORS OF TRAINS

  • V. S. Yazinin,
  • A. M. Baranovskiy,
  • A. A. Vorobyev,
  • I. Yu. Romanova

DOI
https://doi.org/10.12731/2227-930X-2023-13-1-267-287
Journal volume & issue
Vol. 13, no. 1
pp. 267 – 287

Abstract

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Background. This article presents the results of the analysis of the mechanism of artificial neural networks on Russian railways for the control of current collectors of trains. A critical assessment of the situation was carried out and a forecast of the further development of neural network technologies in railway transport was given. Materials and/or research methods. Methods of mathematical modeling, system analysis, data analysis, comparison, systems theory, as well as architecture and mathematical model of neural network and information standards were used. Results. A model of the information system for monitoring the characteristics of current collectors has been developed, the algorithm of its operation and the functionality of the software package of the model of the system for monitoring the characteristics of current collectors of rolling stock have been described. The algorithm of neural network operation for determining the characteristics of electric rolling stock current collectors is presented. The analysis of the prospects for the use of artificial neural networks in the field of railway transport has been carried out, and a simulation model of an information system for monitoring the characteristics of current collectors of electric rolling stock of railway transport has been created. Conclusion. The article examines the positive experience of JSC “Russian Railways” on the introduction of neural network technologies in technological processes and further prospects for the use of neural network technologies on Russian railways. The necessity of intensifying the development and implementation of neurotechnologies to solve a wide range of tasks, including tasks to optimize the organization of maintenance and repair of rolling stock, is noted. The effectiveness and relevance of the use of artificial neural networks in various fields of activity, especially on Russian railways, is shown, and a model of an information system in the field of monitoring the characteristics of current collectors is proposed.

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