New Delhi: Indian Railways has begun deploying advanced Artificial Intelligence (AI) and Machine Learning (ML)-based monitoring systems to strengthen safety and improve operational efficiency across its network.Â
The move is part of the railways’ ongoing push toward adopting smart technologies for real-time monitoring of trains, tracks, and infrastructure.
The information was shared in the Lok Sabha by Union Minister for Railways, Information and Broadcasting, and Electronics and Information Technology, Ashwini Vaishnaw.
AI-Based Train Inspection Systems
One of the key technologies being introduced is the Machine Vision Inspection System (MVIS), an AI and ML-driven system designed to detect loose, hanging, or missing components in moving trains. Currently, three MVIS units are installed in Northeast Frontier Railway, two in Dedicated Freight Corridor Corporation of India Limited (DFCCIL), and one in South East Central Railway on a pilot basis for freight stock.
In addition, a Memorandum of Understanding (MoU) has been signed between Indian Railways and DFCCIL to introduce four more MVIS systems across the railway network. The Research Designs and Standards Organisation (RDSO) is also working with industry partners to develop MVIS technology for broader rolling stock applications.
Systems for Wheel and Bearing Monitoring
To detect defects in train wheels, Indian Railways has deployed the Wheel Impact Load Detector (WILD) system. This way-side inspection technology measures the impact of wheels on tracks to identify damaged or defective wheels in rolling stock. A total of 24 WILD systems have been installed across the network.
Another monitoring tool, the Online Monitoring of Rolling Stock (OMRS) system, tracks the condition of bearings and wheels. At present, 25 OMRS units have been installed, including one at Sirpur Kaghaznagar in the Secunderabad Division of South Central Railway.
Smart Track Monitoring with AI
Indian Railways has also introduced Integrated Track Monitoring Systems (ITMS) to inspect and monitor track infrastructure. The system uses machine learning and image processing to detect defects in rails, sleepers, and fastening components.
Currently, three ITMS systems are deployed to record and analyse track conditions. The technology helps improve track maintenance planning, enhances safety, and ensures better reliability of railway assets.
Drone Monitoring and Fog Vision Technology
The railways have started pilot testing drone-based monitoring of Overhead Equipment (OHE) with thermal imaging in the Raipur division. The project is being further developed in collaboration with the Indian Institute of Technology Madras to create an AI-enabled aerial inspection system.
Another innovative system under development is TRI-Netra (Terrain Imaging for Locomotive Drivers). Developed by RDSO, the technology combines optical cameras, infrared sensors, and radar or lidar systems to provide enhanced real-time visibility for locomotive pilots during fog, rain, or other poor weather conditions.
Rail Tech Policy to Encourage Innovation
To accelerate the adoption of new technologies, Indian Railways has introduced the Rail Tech Policy. A dedicated portal has also been launched to allow startups and innovators to submit technology proposals.
The policy allows innovators to submit detailed proposals through a single-stage process, including self-initiated technology solutions. It also offers financial support through a 50:50 cost-sharing model between Indian Railways and innovators, with grants for prototype development, trials, and scaling up promising technologies.
The initiative aims to fast-track innovation and strengthen the role of technology in building a safer and more efficient railway system in India.
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