Russian Nuclear Power Plants Deploy Advanced AI Platform for Predictive Equipment Maintenance
Employees at the Novovoronezh, Leningrad, and Beloyarsk nuclear power plants have begun pilot operations of AtomMind, a Russian predictive analytics platform.
Rosatom’s Fuel Division has developed software that continuously analyzes thousands of operating parameters and alerts plant operators to hidden equipment defects long before they become apparent. Deploying these algorithms at nuclear power plants makes it possible to move beyond scheduled maintenance toward predictive maintenance.
Artificial Intelligence on Guard
Nuclear power plants are among the most complex engineering systems, with thousands of pumps, compressors, turbines, and generators operating simultaneously. Traditional maintenance relies on fixed schedules for preventive maintenance and repairs. Equipment is taken offline and inspected after a set period of operation, even when it has not yet reached the end of its service life. This approach drives up costs and takes generating capacity out of service unnecessarily.
The AtomMind platform takes a different approach. It uses machine learning to monitor equipment condition continuously, processing large volumes of telemetry data collected from a wide range of sensors.
Pilot operations at three different types of nuclear power plants will allow the system to be tested under real-world conditions. Novovoronezh and Leningrad Phase II are equipped with VVER-1200 reactors, while Beloyarsk operates BN-600 and BN-800 fast reactors. The variety of plant systems will give the platform a broad dataset for further training and fine-tuning its models to the characteristics of each piece of equipment.
Predictive Diagnostics Algorithms
The system is designed to detect anomalies at the earliest possible stage. Human operators and standard emergency protection systems typically respond when parameters have already moved outside their permissible ranges. By then, a defect may have progressed to the point where the damage cannot be reversed.
AtomMind’s neural network takes a different approach: it builds a virtual baseline of normal operating conditions for each monitored component. It accounts for the relationships among hundreds of parameters. If sensor readings begin to deviate from the model’s calculated values, the platform immediately flags an anomaly.
The algorithms do more than issue alerts. They automatically identify the likely cause and forecast how the situation may develop. Operators receive a notification identifying the specific component, the type of defect likely to occur, and the estimated time remaining before a possible failure. This gives maintenance teams valuable lead time to prepare spare parts and schedule repairs for a convenient maintenance window.
Shifting to Condition-Based Maintenance
Using AtomMind can largely prevent unexpected equipment failures, which can cause substantial losses for nuclear power plants and require costly emergency repairs. An unplanned reactor shutdown can cost millions of rubles.
Condition-based maintenance means equipment does not have to be repaired simply because a scheduled interval has elapsed. Instead, resources can be focused on components that actually show signs of wear. This approach reduces procurement costs and eases the workload on plant personnel.
The platform also builds a knowledge base of recurring equipment faults. As it accumulates more data, the neural network can improve its predictive accuracy over time. Operators gain a reliable digital assistant that takes on the routine work of analyzing large datasets.
Scaling Up the Digital Ecosystem
The current pilot phase is intended to verify the technology’s reliability under actual operating conditions. Engineers from Rosatom’s Fuel Division are working with specialists at the three Russian nuclear power plants to compare the platform’s predictions with the results of physical inspections and nondestructive testing. If the pilot projects deliver positive results, the platform could be rolled out across all nuclear power plants in Russia.
AtomMind is part of Rosatom’s broader strategy to digitalize Russia’s nuclear power industry. The platform is being integrated with process control systems and enterprise maintenance-planning modules, creating an end-to-end digital environment.
The successful use of predictive analytics demonstrates the progress Russian companies have made in industrial AI. In the past, similar solutions were purchased from foreign suppliers. Rosatom now has its own technology stack, which helps safeguard the information security of critical infrastructure and creates opportunities to export digital services to international partners building nuclear power plants abroad.
