Railway Track Fault Detection System Using IOT
Anil Kumar C1, Akash C2, Chetan R3, Darshan B4, Harsha GN5
1Assistant Professor, 2Final year Student, 3Final year Student , 4Final year Student, 5Final year Student Department of Electronics and Communication Engineering,P E S institue of technology and management,Shimoga
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Abstract - The Railway Track Fault Detection System using the Internet of Things (IoT) aims to enhance the safety and reliability of railway networks by enabling real-time monitoring of track conditions. The proposed system uses IoT sensors, such as vibration, temperature, and displacement sensors, to detect potential faults such as cracks, misalignments, and other track defects that could pose risks to the safety of train operations. These sensors are strategically placed along the railway tracks and continuously transmit data to a central server or cloud-based platform. Machine learning algorithms process this data to identify anomalies or patterns indicative of faults, allowing for early detection and proactive maintenance.
The system includes features such as GPS tracking to pinpoint the exact location of faults and alerts sent to railway operators for timely action. Additionally, the use of IoT enables the seamless integration of real-time data, enhancing operational efficiency and reducing the need for manual inspections. The proposed solution also allows for predictive maintenance, as it can estimate the remaining lifespan of the track and identify sections that require attention before they cause significant issues. Overall, this system significantly reduces the risk of accidents, minimizes operational downtime, and lowers maintenance costs, contributing to safer and more efficient railway transport systems.
Key Words: The key concepts in the Railway Track Fault Detection System using IoT include Railway Track, Fault Detection, and the integration of Internet of Things (IoT) technology for real-time monitoring. The system utilizes various sensors, such as vibration sensors, temperature sensors, and displacement sensors, to detect potential track issues. Through anomaly detection techniques and the use of machine learning algorithms, the system can identify faults early, enabling predictive maintenance. It processes the collected data using data processing methods and incorporates GPS tracking for precise fault localization. The system sends maintenance alerts to operators, enhancing operational efficiency and safety by addressing track condition issues before they lead to accidents. The use of a cloud platform allows seamless data integration and decision-making, benefiting the overall railway network through cost reduction and improved system reliability.
