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This article discusses a modern method for diagnosing track circuits using broadband signals. The relevance of this research stems from significant shortcomings of traditional track circuits, such as low information content, strong dependence on ballast condition, and high operating costs, which lead to significant economic losses and risks to traffic safety. The proposed approach using broadband signals enables continuous monitoring of track conditions in real time, precisely localizing defects such as breaks, current leaks, insulation deterioration, and microcracks, which is impossible using traditional methods. This not only improves traffic safety but also significantly reduces the number of false alarms. The paper presents a detailed mathematical model of broadband signal propagation, taking into account key attenuation factors: the effect of current leakage through ballast, sleepers, and track connections. A comprehensive analysis of the signal's frequency characteristics was conducted, including a study of the skin effect in rails and the dependence of the complex ballast impedance on external conditions such as humidity and temperature. To minimize signal loss, a comprehensive approach based on adaptive frequency range optimization and data processing algorithms was proposed. The study focuses on the principle of matched filtering, which maximizes the signal-to-noise ratio at the filter output through effective signal compression and intelligent parameter selection. A structural diagram of the diagnostic system with a detailed description of its functional components was developed and presented. The study results demonstrate that the implementation of a monitoring system based on broadband signals enables continuous monitoring of infrastructure conditions without interrupting service, significantly improving the accuracy and speed of defect localization. Practical implementation of the developed method can significantly reduce operating costs and improve the overall reliability, safety, and throughput of rail transport.

The article discusses the issues of improving safety when performing production work near railway tracks, which are associated with a high level of risk for personnel. The purpose of this work is to develop and substantiate a promising system for notifying work crews about the approach of rolling stock, which can increase the effectiveness of safety measures at railway infrastructure facilities. The methodological basis of the study was the analysis of the causes of accidents related to the shortcomings of existing warning systems, as well as modeling the signal generated by an electromagnetic sensor when the wheel crest passes over it. To implement the system, it is proposed to use the LoRaWAN network and LoRa radio modules combined with Arduino microcontrollers. As part of the research, a structural diagram of the system and a working layout have been developed, including transmitting devices with a composition detection sensor and receiving devices integrated into workers' signal vests. As a result, a mathematical model of the signal generated by a change in the magnetic flux caused by the wheel approaching the sensor has been obtained. The dependence of the magnitude of the electromotive force on the position of the wheel relative to the coil is proved. Graphs of the dependence of the magnetic flux and the induced signal on the transit coordinate are constructed. The proposed system can be used as the main or auxiliary means of notification during repair and other work near the tracks. The conclusion is made about the prospects of the development for implementation in the industrial safety system of Russian Railways. The proposed system has high potential efficiency, can function independently and independently of weather conditions, and also serve as a reliable complement to the work of alarm systems. The introduction of this technology can significantly reduce occupational injuries at railway infrastructure facilities.