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Recent Research Reviews Journal

IoT-Based Railway Rail Fitting Detection Using Infrared Algorithm

Open Access
Published: 27 April, 2026
Pages: 15-29
  • Vinodhini S. , Vinodhini S.
    Assistant Professor, Department of Information Technology, Velammal Engineering College, Surapet, Chennai, India
    Assistant Professor, Department of Information Technology, Velammal Engineering College, Surapet, Chennai, India
  • Sundhar Raj R. , Sundhar Raj R.
    Student, Department of Information Technology, Velammal Engineering College, Surapet, Chennai, India
    Student, Department of Information Technology, Velammal Engineering College, Surapet, Chennai, India
  • Jeeva S. Jeeva S.
    Student, Department of Information Technology, Velammal Engineering College, Surapet, Chennai, India
    Student, Department of Information Technology, Velammal Engineering College, Surapet, Chennai, India
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How to Cite

S., Vinodhini, Sundhar Raj R., and Jeeva S. 2026. “IoT-Based Railway Rail Fitting Detection Using Infrared Algorithm”. Recent Research Reviews Journal 5 (1): 15-29. https://doi.org/10.36548/rrrj.2026.1.002.
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Keywords

Rail Fitting Detection
Infrared Sensing
IoT
Railway Safety
GSM Alert
QR Code Localization

Abstract

The reliability of railway lines relies heavily on rail fittings such as clips and fasteners that keep the rails firmly attached to the sleepers. Failure in rail fittings such as clips leads to an increased risk of derailments. This paper highlights the development of a monitoring system based on infrared sensing technique, microcontroller and cellular network to detect rail faults caused by fittings failure. When the readings in the reflected signal from the fittings become too low or erratic, an infrared sensor signals the Arduino Nano microcontroller to identify a fault. On confirmation of the fault, Raspberry Pi microcomputer triggers the camera to read the Quick Response code from the railway and locate the fault's location before the information is sent to maintenance crews through GSM module. In testing this technology using the model rail track, the proposed system successfully detected faults in eight test cases where it achieved 100% accuracy in detecting missing fittings and 92% for loose fittings. The entire system operates automatically and uses low-cost parts.

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