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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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Статистические характеристики ветрового волнения на юго-восточном побережье о. Сахалин по инструментальным измерениям 2006-2009 гг

Известия РАН. Физика атмосферы и океана. 2014. Т. 50. № 2. С. 242–250.
Pelinovsky E., Kurkin A. A., Кузнецов К. И., Ковалев П. Д.

The results of windwave measurements with bottompressure sensors are discussed. These mea surements were performed at the southeastern coast of the Island of Sakhalin in 2006–2009. The problems of converting data obtained with these sensors into waterlevel displacent in the context of the linear theory are noted. Twenty records with a duration of 2 weeks to 3 months were obtained. These records contain from 150000 to 1200000 waves for three different observation points located at the southeastern coast of Sakhalin in the regions of the village of Vzmor’ye, Lake Izmenchivoye, and the Cape of Ostryi. The estimates obtained for waveheight distributions were approximated by the Weibull distribution. They were compared to the the oretical Rayleigh, Forristall, Weibull, and Glukhovskii distributions. As one would expect, in the region of small wave heights, all distributions prove to be close to one another, and, for large wave heights, the Weibull distribution is preferred

Priority areas: mathematics engineering science
Language: Russian
Full text
Keywords: морские природные катастрофы
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