Issue |
E3S Web of Conf.
Volume 508, 2024
International Conference on Green Energy: Intelligent Transport Systems - Clean Energy Transitions (GreenEnergy 2023)
|
|
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Article Number | 03001 | |
Number of page(s) | 12 | |
Section | IoT, AI and Data Analytics | |
DOI | https://doi.org/10.1051/e3sconf/202450803001 | |
Published online | 05 April 2024 |
Microprocessor system control stage: railway tracks example
Ukrainian State University of Railway Transport, Feuerbach Square 7, 61050, Kharkiv, Ukraine
* Corresponding author: almammadovamehriban@gmail.com
This paper introduces a novel method of managing railway tracks by utilizing a control system based on microprocessors. The creation and deployment of an advanced system intended to improve the effectiveness, security, and dependability of railway operations is the main objective. The suggested system makes use of cutting-edge microprocessor technology to keep an eye on, manage, and repair a number of railway infrastructure components, such as train movements, signal operations, and track integrity. The system offers an automated approach to identify and resolve any problems such track defects, signal failures, and unwanted access through real-time data collecting and processing. Additionally, the integration of predictive maintenance algorithms helps in preemptive identification of wear and tear on tracks, thereby reducing the likelihood of accidents and improving overall service quality. The paper details the architectural design of the system, its operational mechanisms, and the results of a series of simulations and real-world tests conducted to validate its effectiveness. The findings demonstrate significant improvements in operational efficiency, reduced maintenance costs, and enhanced passenger safety, illustrating the potential of microprocessor-based systems in revolutionizing railway infrastructure management.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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