IoT-Enabled Framework for Intelligent Railway Track Fault Detection and Localization

Paper ID: EIJTEM_2025_12_4_154-161

Author's Name: D Manusha, Dr. B. Prabhakar

Volume: 12

Issue: 4

Year: 2025

Page No: 154-161

Abstract:

Railway transportation is crucial for passenger and freight movement, yet undetected track faults often cause derailments and disruptions. Traditional inspection methods are manual, time-consuming, and prone to oversight. To overcome these issues, this study presents an IoT-based real-time monitoring system for detecting and localizing railway track faults through acoustic signal analysis. The setup integrates IR sensors, metal detectors, and an acoustic module interfaced with an Arduino Mega, supported by an ESP-based IoT module for cloud data transmission and remote monitoring. Acoustic analysis identifies anomalies such as cracks or misalignments by interpreting variations in sound signatures. Additionally, RF communication triggers immediate on-site alerts via a buzzer. Experimental results confirm the system’s accuracy, scalability, and cost-efficiency in detecting structural irregularities, making it a robust solution for predictive railway maintenance and smart infrastructure management

Keywords: IoT, Railway Track Fault Detection, Acoustic Analysis, Arduino Mega, ESP8266, Real-Time Monitoring

References:

[1] Sreedharan, E., and Arora, S., “Enhancing Track Safety using Track Recording Vehicles: Indian Railways Approach,” Indian Railway Journal, vol. 57, no. 2, pp. 89–95, 2011.
[2] Giannopoulos, A., Fan, Y., and Forde, M. C., “Railway Track Fault Detection Using Acoustic Emission Techniques,” NDT & E International, vol. 37, no. 3, pp. 143–150, 2004.
[3] Duarte, C., Ferreira, P., and Mendes, A., “Application of Wavelet Transform for Railway Track Fault Detection Using Acoustic Signals,” Journal of Transportation Technologies, vol. 7, no. 4, pp. 408–420, 2017.
[4] Petrovic, A., Novakovic, A., and Pavlovic, D., “Hybrid Sensor Framework for Railway Infrastructure Monitoring,” IEEE Sensors Journal, vol. 19, no. 22, pp. 10750–10758, Nov. 2019.
[5] Wang, J., Li, M., and Zhang, Y., “Real-Time Track Condition Monitoring Based on Wireless Sensor Networks,” Sensors, vol. 16, no. 5, pp. 683–692, 2016.
[6] Ahmed, R., Begum, S., and Rahman, M., “IoT-Based Real-Time Railway Track Fault Detection System Using Cloud Analytics,” International Journal of Computing and Digital Systems, vol. 7, no. 1, pp. 1–10, Jan. 2018.
[7] Kundu, A., Roy, S., and Das, A., “Vibration-Based Fault Classification in Railway Tracks Using Random Forest Model,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 12, pp. 5230–5238, Dec. 2020.
[8] Kumar, S., and Bansal, D., “Predictive Maintenance of Railway Tracks Using Machine Learning: A Deep Learning-Based Approach,” Procedia Computer Science, vol. 167, pp. 2343–2352, 2020.
[9] Choudhury, A., and Rajan, R., “Vibration-Based Rail Crack Detection System Using IoT and GSM Technology,” International Journal of Engineering Research & Technology (IJERT), vol. 6, no. 4, pp. 405–409, 2017.
[10] Yadav, N., and Singh, V., “Railway Track Fault Detection Using Metal Sensor and GSM Communication,” International Journal of Emerging Technology and Advanced Engineering, vol. 10, no. 9, pp. 33–39, Sep. 2020.
[11] Sun, Y. H., and Hwang, J. N., “Railway Track Condition Monitoring by Acoustic Signature Analysis,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2459–2462, 2000.
[12] Zhang, W., Liu, T., and Li, Q., “Rail Defect Detection Using Acoustic Signals and Deep Learning,” IEEE Access, vol. 6, pp. 41794–41803, 2018.
[13] Li, H., Zhao, G., and Wang, J., “Hilbert-Huang Transform-Based Fault Detection for Rail Systems,” Mechanical Systems and Signal Processing, vol. 142, pp. 106720, 2020.
[14] Anand, R., Gupta, M., and Jaiswal, A., “Multisensor Fusion for Railway Crack and Obstacle Detection,” International Journal of Engineering and Advanced Technology (IJEAT), vol. 10, no. 3, pp. 119–123, Feb. 2021.
[15] Zhao, F., Xu, L., and He, Z., “An IoT-Based System for Railway Safety Monitoring,” Journal of Network and Computer Applications, vol. 130, pp. 108–120, 2019.

View PDF