HSE University to Develop Predictive Analytics System for Icebreaker Motors
Industrial automation is one of the key applications of artificial intelligence. A predictive analytics system for large electric motors is among the solutions being developed for the industry as part of HSE University’s Strategic Technological Project ‘Multi-Agent Platform of AI Solutions for Industry-Specific Tasks.’ What is predictive analytics, how can it improve the operation of electric motors, and what specialists joined forces to develop this technology? Anton Zarubin, Dean of the School of Computer Science, Physics, and Technology at HSE University–St Petersburg and the project development coordinator, explains in this interview with the HSE News Service.
Why We Need Predictive Analytics for Electric Motors
Predictive analytics is an important component of an electric motor’s control and maintenance systems.
It combines data from various sensors installed in the motor, such as temperature and vibration sensors, with mathematical algorithms that process this data. Using machine learning and AI techniques, the system can identify existing problems in the motor’s operation and predict potential failures before they occur. This approach makes it possible to use the motor’s service life more efficiently and reduce warranty and maintenance costs.
Anton Zarubin
Predictive analytics systems can be used with electric motors of various sizes. The system we are developing is designed for large electric motors whose diameter can exceed a person’s height. These Russian-made motors are used by water utilities—at pumping stations, wastewater treatment plants, and other critical infrastructure facilities—as well as in water transport, including icebreakers. In both areas, ensuring technological sovereignty and strengthening Russia’s technological leadership are of particular importance.
A Quantum Leap in the Evolution of Industrial Automation
The team formed in spring 2026 to develop this product consists of about ten people, most of them employees of HSE University–St Petersburg. All team members have experience conducting research and implementing applied projects in industrial automation, telecommunications, and other fields, as well as working with industrial partners.
Our current partners include manufacturers of electric motors for the shipbuilding industry and the Russian Association of Water Supply and Sanitation. Both have expressed interest in our developments and their potential applications.
Industrial automation—a set of technologies that enable production processes to be controlled with minimal direct human involvement—has existed since the Soviet era. Today, however, the field is experiencing a quantum leap.
Its transformation is being driven not only by advances in AI and software but also by the adoption of cutting-edge engineering solutions. The predictive analytics system for electric motors that we are developing as part of the strategic technological project is just one of the areas in which these technologies can be applied.
Pilot AI Models and Test Bench
Our team has already developed models simulating the operation of an electric motor in a pumping unit under ideal conditions. A test bench has also been installed to collect data on electric motor performance, and the first dataset has been collected to train a predictive analytics model.
By the end of this year, we plan to develop AI models capable of detecting and classifying more than ten types of defects and predicting the remaining service life of electric motors. We also plan to create digital twins of electric motors and a prototype cloud-based monitoring platform with a web interface for engineers, capable of connecting up to 1,000 motors.
In October, we plan to complete the development of a pilot version of an AI-based predictive diagnostics system for large electric motors. The system is scheduled to be deployed at a test site in October, followed by pilot testing in November. Also in November, our work will be presented at the international AI Journey 2026 Conference. The presentation will focus on the AI hardware and software system for predictive diagnostics of large electric motors based on current-spectrum analysis.
Andrey Darkshevich,
Deputy Director of the AI and Digital Science Institute at HSE FCS
'The predictive analytics project for electric motors is not only an advanced industrial solution but also a platform for integrating several applied research results developed at HSE University. This year, we are re-using our Predict Core predictive analytics platform. Danil Shvetsov is leading the project to develop a system for visualising monitoring results and supporting engineering decision-making.
In the future, we could potentially incorporate technologies developed as part of the Strategic Technological Project ‘Trusted 6G Communication Systems Technology Suite’ to enable remote monitoring of equipment status.
The predictive diagnostics system for large electric motors is being developed as part of the Strategic Technological Project (STP) ‘Multi-Agent Platform of AI Solutions for Industry-Specific Tasks.’ The strategic technological projects are part of HSE University’s Development Programme for 2025–2036, which won the Priority 2030 strategic academic leadership competition.
See also:
How to Assess Students’ Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
5th Fall into ML Conference to Bring Together Leading AI Researchers
The AI and Digital Science Institute at the HSE Faculty of Computer Science invites researchers, developers and everyone shaping the future of technology to the fifth, anniversary edition of the Fall into Machine Learning conference (Fall into ML 2026). The event will take place on October 23–24, 2026, at the HSE Cultural Centre in Moscow and will become the key meeting point for Russia’s AI community.
HSE University Expands Cooperation with Malaysia in Technology Foresight
HSE University researchers will take part in a study of the future of engineering education in Malaysia, while the Malaysian Industry-Government Group for High Technology (MIGHT) will use the iFORA big-data analysis system to validate the findings of its foresight research. These are the outcomes of a visit by HSE representatives to Kuala Lumpur.
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.
Laboratory of Future Networks: HSE Telecommunications Research Institute Develops 5G/6G Research Testbed
The 5G/6G testbed at the HSE Telecommunications Research Institute is becoming a research platform, an educational laboratory, and a foundation for developing new software components for future networks. It makes it possible not only to observe how a mobile network operates, but also to change its operating conditions and measure the results: data-transmission speed, latency, errors, radio-resource utilisation, and other parameters. Based on the testbed, researchers plan to develop MIMO, O-RAN, xApp, and IAB technologies, as well as experiment with artificial intelligence.
‘Hedgehog’ Versus ‘Relatives’: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech
Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
HSE Researchers Create New Corpus of Early Child Speech in Russian
Researchers at the HSE Centre for Language and Brain have presented RusLan-M, an open multimedia corpus that makes it possible to trace the development of early child speech in Russian from first words to the emergence of complex grammatical constructions. The database contains around 41 hours of video recordings and more than 35,000 child utterances. The new resource will help researchers study more precisely how children acquire Russian and, in the longer term, develop more reliable tools for assessing speech development. The study has been published in Language Resources and Evaluation.
Hybrid Intelligence: Competencies in the Age of AI Discussed at Technoprom-2026
Artificial intelligence is not creating new professions, but rather transforming the nature of existing ones. This was the conclusion reached by participants in the panel session ‘Hybrid Intelligence: Digital and Human Drivers of Development,’ organised by the Institute for Statistical Studies and Economics of Knowledge (ISSEK) at HSE University as part of the 13th International Forum of Technological Development (Technoprom-2026). The experts discussed how the nature of work is changing, which skills are becoming increasingly sought after, and what prevents companies from fully capitalising on new technologies.
Scientists Develop New Solution for 6G Communication Systems
A terahertz neuromorphic circuit developed by scientists at HSE University could make 6G communication systems both more accurate and energy-efficient. The circuit enables indoor tracking of mobile devices with an accuracy of up to 99%. The results were presented at PIERS 2026, an international symposium on Photonics and Electromagnetism held in China.