MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085386 A) filed by Sri Eshwar College Of Engineering on July 11, 2026, for A Digital Twin-Driven Edge Ai Framework With Distributed Iot And Fiber Optic Sensing For Real-Time Railway Track Monitoring And Predictive Maintenance.
Inventors include Dr. R. Babitha Lincy; Ms. Minu Balakrishnan; Dr. H. Anandakumar; and Mrs. K. Gowthami.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: One of the most significant means of transportation is railway, as it contributes to the economic development by efficiently carrying passengers and goods. The railway line in India is over a thousand kilometers in length and is subjected to end-to-end mechanical load, temperature change, rainfall, vibration, and environmental degradation. These gradually cause cracks in the rails, track deformation, loosening of fasteners, ballast degradation, and alignment problems, which can pose a threat to derailment and service disruptions. Thus, it is crucial to continuously monitor and maintain railway tracks to guarantee railway operation safety, reliability, and the ability of managing the railway infrastructure cost effectively. The traditional railway track inspection methods are mostly based on manual patrols, planned maintenance and periodic inspection vehicles. All of these have enhanced railway safety, but they are labor intensive, time consuming and sometimes don't catch defects that occur between inspections. Furthermore, many of the current monitoring solutions are decentralized and cloud-based, which are susceptible to network lag and poor connectivity in distant locations. These restrictions emphasize the need for an intelligent, real-time and decentralized railway monitoring system that allows continuous evaluation of the health of the railway track. The next-generation of railway infrastructure monitoring is presents a promising solution through recent developments in Distributed Internet of Things (IoT) sensor networks, Fiber Optic Sensing (FOS), Edge Artificial Intelligence (Edge AI) and Digital Twin technology. Distributed sensor arrays continuously monitor vibration, strain, temperature, humidity and displacement; Fiber Optic Sensing technologies monitor long distance track sections with high accuracy, detecting changes in vibration, strain and temperature. The Edge AI microcontroller performs the necessary sensor data collection, which allows for real-time anomaly detection, sensor fusion and predictive analysis without ongoing communication with the cloud. The extracted data is then used to update a Digital Twin, a virtual representation of the railway assets that is updated in real time to visualize, assess, and plan for predictive maintenance. The proposed framework combines distributed sensing, intelligent edge computing and Digital Twin technology in synergy to continuously monitor the condition of the railway tracks, detect early defects in the tracks, and forecast possible defects in the tracks before they reach a critical state. The system enables real-time decision making, improve railway safety, lower maintenance expenses, decrease service disruptions and increase the reliability and sustainability of modern railway infrastructure.
Disclaimer: Curated by HT Syndication.
