AI To Transform Railway Safety Systems
RAILWAYS & METRO RAIL

AI To Transform Railway Safety Systems

Artificial intelligence and machine learning are reshaping business operations by automating routine tasks, improving efficiency and enabling real-time decision making. In India, banking, financial services, manufacturing and information technology sectors have adopted AI at scale, while the railway sector has been slower despite government efforts to modernise ageing infrastructure. Much of the rail network still depends on manual inspection and maintenance even as Automatic Train Protection systems are introduced.

Bengaluru-based Nova Control Technologix, a subsidiary of e2E Rail, has partnered Tata Elxsi to integrate AI into signalling systems, coaches and other equipment to develop predictive systems that identify potential failures. Nova Control Technologix plans to invest Rs one billion (bn) over the next two to three years to develop advanced signalling and AI enabled coach technologies, and Tata Elxsi will supply cybersecurity and data protection expertise. The firms say the aim is to move from reactive maintenance to continuous self monitoring and early fault prediction.

They are also developing digital twin technology to simulate and validate AI models before deployment across Indian Railways' 0.132 million kilometres (mn-km) network. Tata Elxsi has built AI into the architecture from the design stage to create a modular, scalable and adaptable platform. The architecture is intended to allow advanced algorithms that support decision making for safety and predictive maintenance.

Industry estimates indicate Indian Railways spends 65 to 70 per cent of its maintenance budget on existing assets, and experts say wider adoption of AI driven predictive maintenance could reduce that share below 50 per cent. Indian Railways currently spends around Rs one trillion (tn) annually on safety and maintenance. The companies contend that AI can make systems self diagnostic and more autonomous by identifying patterns, predicting failures and recommending corrective action, which should improve asset availability and punctuality.

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Artificial intelligence and machine learning are reshaping business operations by automating routine tasks, improving efficiency and enabling real-time decision making. In India, banking, financial services, manufacturing and information technology sectors have adopted AI at scale, while the railway sector has been slower despite government efforts to modernise ageing infrastructure. Much of the rail network still depends on manual inspection and maintenance even as Automatic Train Protection systems are introduced. Bengaluru-based Nova Control Technologix, a subsidiary of e2E Rail, has partnered Tata Elxsi to integrate AI into signalling systems, coaches and other equipment to develop predictive systems that identify potential failures. Nova Control Technologix plans to invest Rs one billion (bn) over the next two to three years to develop advanced signalling and AI enabled coach technologies, and Tata Elxsi will supply cybersecurity and data protection expertise. The firms say the aim is to move from reactive maintenance to continuous self monitoring and early fault prediction. They are also developing digital twin technology to simulate and validate AI models before deployment across Indian Railways' 0.132 million kilometres (mn-km) network. Tata Elxsi has built AI into the architecture from the design stage to create a modular, scalable and adaptable platform. The architecture is intended to allow advanced algorithms that support decision making for safety and predictive maintenance. Industry estimates indicate Indian Railways spends 65 to 70 per cent of its maintenance budget on existing assets, and experts say wider adoption of AI driven predictive maintenance could reduce that share below 50 per cent. Indian Railways currently spends around Rs one trillion (tn) annually on safety and maintenance. The companies contend that AI can make systems self diagnostic and more autonomous by identifying patterns, predicting failures and recommending corrective action, which should improve asset availability and punctuality.

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