| Issue |
E3S Web Conf.
Volume 411, 2023
VI International Conference on Actual Problems of the Energy Complex and Environmental Protection (APEC-VI-2023)
|
|
|---|---|---|
| Article Number | 01020 | |
| Number of page(s) | 8 | |
| Section | Issues of the Energy Complex | |
| DOI | https://doi.org/10.1051/e3sconf/202341101020 | |
| Published online | 10 August 2023 | |
Development of a multi-channel classifier of rail line states
1 Samara State Transport University (SSTU), 2V, Svobody Street, Samara, 443066, Russia
2 Povolzhskiy State University of Telecommunications and Informatics, Samara, 443010, Russia
3 Voronezh branch of the Federal State-Funded Educational Institution Rostov State Transport University, Voronezh, 394026, Russia
* Corresponding author: alzolkin@list.ru
The article deals with the construction of a three-channel invariant classifier that has the properties of classifying the states of rail lines into a set of classes that are invariant to changes in the longitudinal resistance of the rail line and the transverse conductivity of the insulation of the ballast material. Invariance is achieved taking into account the change in the transverse conductivity of the insulation and the longitudinal resistance of the rail line while compiling systems of equations of state for rail lines, which are the decisive functions of the classifier. The article shows that the three-channel method allows for the correct recognition of all three classes of rail line states by three decision functions with arguments - voltages and currents at the input and output of the rail line. The block diagram of the operation algorithm of the three-channel classifier of the states of the rail lines allows to form the recognition process and the majority classification depending on the states of the channels. The feasibility of the algorithm is confirmed by simulation studies on a mathematical model and graphical results.
© The Authors, published by EDP Sciences, 2023
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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