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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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Интегрированный автоматизированный контроль для систем радиопеленгации

С. 109–110.
Журков А. П.

The basic characteristics of the processes of monitoring and diagnosing the automatic radio direction-finding systems (ARPS). The requirements of monitoring and diagnosing systems-finding. The principles of organization of the processes of monitoring and diagnosing ARPS. The variants of the technical realization.

Language: Russian
Keywords: радиопеленгация

In book

Научно-техническая конференция студентов, аспирантов и молодых специалистов НИУ ВШЭ. Материалы конференции
Научно-техническая конференция студентов, аспирантов и молодых специалистов НИУ ВШЭ. Материалы конференции
М.: МИЭМ НИУ ВШЭ, 2014.
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Added: March 5, 2014
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