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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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Optical Angular Displacement Sensors

P. 312–315.
Alexander L. Tuv, Yury I. Gudkov, Vladimir N. Azarov

The paper is devoted to the research and development of a contactless optical sensor for instrumental means of product quality control. The paper presents results of the study of sensitivity of incoherent optical OTDR type sensors to the angular displacement of the reflecting surface. The results of experimental studies of the transfer characteristics of the sensor with transceiver modules of various configurations are presented. Variants of the structure of the measuring channel of the sensor to obtain the maximum range of measured angles are offered. The influence of non-informative parameters on the measurement results is analyzed. The results of mathematical modeling of the angular displacement sensor based on the laws of geometric optics are presented.

Language: English
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Keywords: optical sensorsPhotodetectorsLight emitting diodesOptical variables measurementDisplacement measurementOptical surface waves

In book

Proceedings of the 2019 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS)
Proceedings of the 2019 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS)
IEEE, 2019.
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