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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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Room Temperature Propylene Dehydrogenation and Linear Atomic Chains Formation on Ni(111)

Journal of Physical Chemistry C. 2020. Vol. 124. No. 15. P. 8218–8224.
T.V. Pavlova, S.L. Kovalenko, K.N. Eltsov

The structures formed by propylene adsorption on Ni(111) at room temperature are determined by a combination of scanning tunneling microscopy and density functional theory. As a result of the interaction with the Ni(111) surface, propylene molecules are dehydrogenated and coupled into linear hydrocarbon chains. The length of the chains varies from 8 to 60 Å, with the most frequently observed length of 18 Å. At saturated coverage, some chains are closed in rings with a diameter of 6 Å. A C12H12 model is proposed for most often observed chains. We demonstrate the possibility of combining initial propylene molecules into chains after dehydrogenation of the CH3 fragment.

Research target: Chemistry Physics
Priority areas: engineering science
Language: English
DOI
Keywords: DFTSTMNi(111)carbon chainspropylene
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