TechnologyPower and heat generation
Predictive-maintenance software begins pilot at three Russian nuclear plants
Rosenergoatom began trial operation of a domestic predictive-analytics system at Novovoronezh II, Leningrad II and Beloyarsk, testing whether it can flag equipment issues before failure.
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- First published on EPC Feed
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- 2 listed below
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- 2 min
Rosenergoatom began pilot operation of a domestic predictive-analytics system at Novovoronezh II, Leningrad II and Beloyarsk nuclear power stations, Atom Media reported on 22 October. The software uses machine-learning algorithms and neural networks to identify potential equipment problems before they occur. Rosatom says the trial is intended to test the system in live operating conditions at three stations before a decision is made about permanent use. It does not give the trial duration or identify the equipment covered.
The operator expects the system to support reliability by reducing the likelihood of equipment damage and failure and by helping plan maintenance against observed condition. The announcement does not say these outcomes have already been measured. It also makes a key governance point: the operational decision remains with a specialist. The digital tool is described as adding information to the expert’s assessment, rather than automatically controlling equipment or issuing maintenance orders.
Running the trial at three separate stations gives Rosenergoatom a chance to assess system behaviour in more than one plant environment. The release does not describe the underlying data sources, model architecture, alert thresholds, false-positive rate or how plant teams will validate predictions. Without those details, no claim can be made about how early the software identifies degradation or whether it performs equally across different equipment types.
A 2023 AtomMind product reference explains the platform behind the current operating trial. It describes AtomMind as a low-code industrial digitalisation platform that can be integrated into an enterprise IT environment, with tools for collecting and analysing historical and live equipment or process data. Its stated implementation sequence begins with surveying and digitising historical data, then a pilot in which data are cleaned, machine-learning models trained and hypotheses evaluated against metrics. Only after that evaluation does the process move into technical design, deployment, user training and ongoing support. The reference describes integration with SCADA, MES, maintenance, laboratory, ERP and business-intelligence systems. This is a product description, not evidence that every capability or integration has been deployed at the three nuclear stations in October 2026.
The two applications point to different data and oversight questions. Predictive maintenance depends on equipment signals and a reliable baseline for normal operation; video safety analysis depends on camera coverage, detection criteria and human review. The releases do not establish that the systems share data or technology. What they substantiate is a limited operational test at three stations and the operator’s stated plan to evaluate readiness before permanent deployment.
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