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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
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Motivation of Energy Saving Within the Corporate Market Responsibility of Economic Entities

P. 95–105.
Popkova E. G., Inshakov O. V., Bogoviz A. V.

Despite the unconditional importance of the role of the state as the guiding coordinator of the efforts of the society and the energy saving business, the key role in this process belongs to the economic entities that directly implement the energy saving strategy adopted at the national level. Therefore, it is important to involve them in the process and ensure that they are interested in its results. © Springer International Publishing AG, part of Springer Nature 2019.

Language: English
DOI
Text on another site
Keywords: energy sector

In book

Energy Sector: A Systemic Analysis of Economy, Foreign Trade and Legal Regulation
Energy Sector: A Systemic Analysis of Economy, Foreign Trade and Legal Regulation
Vol. 44. , Switzerland: Springer, 2019.
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