AI for nuclear safety: Predicting component remaining useful life

Civo

Feb 16, 2026

As industrial systems become more complex in 2026, the reliability of critical infrastructure depends on shifting from reactive to predictive strategies. In this session from Civo Navigate India, Muthukumar Ganesan, a scientist at the Indira Gandhi Centre for Atomic Research (IGCAR), explores the application of AI and machine learning in securing the future of nuclear energy.

Muthukumar breaks down the vital role of the primary sodium centrifugal pump in Fast Breeder Reactors (FBRs) and how its failure, though rare, can lead to severe operational risks. Drawing on lessons from historical incidents like Three Mile Island, he demonstrates how multivariate time series sensor data can be used to estimate Remaining Useful Life (RUL) and identify degradation patterns like bearing wear or shaft misalignment before they become critical.

The talk highlights a "sovereign-by-design" approach to safety, showcasing how virtual models and physical simulations are used to generate datasets for training deep learning models. Muthukumar also covers essential deployment strategies, including redundant and diversified hardware setups to ensure 100% system reliability in safety-critical environments.

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#CivoNavigate #NuclearSafety #PredictiveMaintenance #AI #MachineLearning #IndiaTech #EnergySecurity

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