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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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Introduction, The European Journal of the History of Economic Thought

The European Journal of the History of Economic Thought. 2022. Vol. 29. No. 6. P. 1003–1007.
Nenovsky N., Penchev P., Kolev S., Trautwein H.

In 2009, the European Journal of the History of Economic Thought (EJHET) and the European Society for the History of Economic Thought (ESHET) launched the initiative to publish a special annual issue of EJHET with a selection of papers presented at the ESHET conference of the year before. In the thirteenth year of this collaboration, the result is one more time a special issue that testifies to the shared commitment of the society and the journal in disseminating a panel of original research in the History of Economic Thought

Language: English
DOI
Keywords: monetary economics micro- and macroeconomics in the1920s and 1930sRussian economic development of the 1860s and 1870s
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