RAILWAY TRACK CRACK DETECTION USING ACOUSTIC WAVES

Keeping railway tracks in good condition is essential for the safe and smooth operation of trains. Cracks that remain undetected can cause major accidents, delays in service, and heavy financial losses. This project proposes an IoT-based railway track crack detection system that uses acoustic wave propagation to identify faults at an early stage with better accuracy. In this system, sensor nodes are installed along the railway track. Each node includes a piezoelectric sensor that sends acoustic waves through the rail and a microphone sensor that receives those waves after they travel through the structure. The data is then uploaded to the Blink IoT cloud, where it can be monitored, stored, and analyzed in real time. A user interface is developed to classify the condition of the track as minor, medium, or major cracks, helping maintenance staff understand the severity quickly and take action. An RFID module is also added to detect train arrival and departure, improving coordination and system awareness. Overall, the proposed system offers a cost-effective, non-destructive, and continuous monitoring solution that strengthens railway safety through early crack detection and remote access to information.