Digital Twin-Based Railway Track Fault Detection Using IOT and AI

Abstract

The increasing demand for safe and efficient railway transportation necessitates the adoption of advanced monitoring and maintenance technologies. This paper presents an intelligent Digital Twin-based framework for railway track fault detection by integrating Internet of Things (IoT) sensors and Artificial Intelligence (AI) techniques. Conventional railway inspection methods are often manual, time-consuming, and lack real-time monitoring capabilities, leading to delayed fault detection and increased risk of accidents. To address these limitations, the proposed system employs multiple IoT sensors, including vibration, ultrasonic, infrared, and environmental sensors, to continuously collect real-time data from railway tracks. This data is transmitted through an ESP32 microcontroller to construct a dynamic digital twin that mirrors the physical track conditions. AI and machine learning algorithms are utilized to analyze sensor data, identify anomalies, and predict potential failures before they become critical. The system also incorporates real-time alert mechanisms through mobile notifications and local indicators such as LCD, buzzer, and LEDs, ensuring immediate response to detected faults. Furthermore, the integration of visual intelligence using AI-based image classification enhances detection accuracy by providing contextual insights. The proposed framework enables predictive maintenance, reduces operational costs, minimizes downtime, and significantly improves railway safety and reliability. Overall, this work demonstrates a scalable and efficient approach toward the development of smart railway infrastructure and next-generation intelligent transportation systems.