Rosatom said on October 22 that a domestic predictive-analytics system had entered trial operation at three Russian nuclear plants: Novovoronezh NPP-2, Leningrad NPP-2 and Beloyarsk NPP. The system is intended to identify potential equipment problems before they occur. It is built on AtomMind, a digital platform developed by Rosatom’s Fuel Division.

According to the release, the software analyzes operating parameters in real time, detects and records changes, identifies possible causes, forecasts how a situation may develop and sends a notification to a specialist. Rosatom says machine-learning algorithms and neural-network support allow complex models to analyze large volumes of data.

“Predictive” does not mean that the system replaces staff decisions. A Rosenergoatom executive said specialists remain responsible for decisions while the digital tool provides additional information about changed parameters, possible causes and issues that warrant attention. The approach is intended to combine earlier visibility of potential changes with human assessment.

Rosatom describes possible benefits including fewer equipment failures, higher reliability and maintenance and repair planning based more closely on actual equipment condition. The October 22 release gives no failure-rate results, false-alert count, maintenance-cost savings or avoided unplanned-outage figures. The three-site trial is meant to test the system under real operating conditions and inform a decision on whether it is ready for permanent operation.

Rosenergoatom says it operates 34 units across 10 active nuclear plants, with combined installed capacity of about 29.8 GW and roughly 19% of Russia’s electricity generation. Those company-wide figures put the trial in context but do not mean the software has been deployed across the fleet or has already produced a measurable nationwide reliability gain. The company says a decision on permanent use will follow assessment of the trial.

Rosatom’s earlier digitalization example at Novovoronezh NPP illustrates a separate application from the 2026 predictive-maintenance pilot. In July 2025 the company said an operator information-support system entered trial operation on Unit 6, providing alerts about process-parameter changes and forecasting possible events up to 30 minutes ahead. It described coverage of 360 process systems and roughly 200 interactive procedures, using data from the unit’s control processes. That system is an operator decision aid, whereas the October 2026 AtomMind deployment is aimed at early detection and prediction of equipment condition across three plants. The distinction matters: both use analytics, but the release does not say they are the same software or that the 2025 system’s scope applies to the new pilot.

The October announcement describes the trial as a test in real operating conditions before a decision on permanent use. It says the broader project also includes creation of a predictive-analytics and data-analysis competence centre within Rosenergoatom’s VNIIAES institute. This is an organizational step alongside software deployment; the release gives no timetable for deciding on fleet-wide adoption. The stated objective—earlier warning to allow more time to assess changes—should be distinguished from proof that a failure was prevented. Reliable evaluation would require published operational measures such as alert quality, maintenance interventions, equipment downtime and comparison periods, none of which the company reported in this announcement.