IoT-Based Railway Track Fault Detection and Localization Using Acoustic Analysis

Abstract

Railway transportation is one of the most efficient and widely used modes of transport worldwide; however, track faults remain a major cause of derailments and accidents. Traditional inspection techniques are manual, time-consuming, and prone to human error. This paper proposes an Internet of Things (IoT)-based system for railway track fault detection and localization using acoustic signal analysis. The proposed system employs distributed acoustic sensors integrated with microcontrollers to collect real-time sound signals from railway tracks. Machine learning techniques are utilized to classify faults based on extracted acoustic features. Experimental analysis demonstrates that the proposed system achieves high accuracy in identifying various track defects and enables real-time localization, thereby improving railway safety and maintenance efficiency.