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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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NaFe0.5Mn0.5PO4–Ge electrochemical system for sodium-ion batteries

Mendeleev Communications. 2023. Vol. 33. No. 3. P. 318–319.
Kudryashova Y., Gavrilin I., Tatiana L. Kulova, Svetlana A. Novikova, Skundin A.

A new electrochemical system for sodium-ion batteries, sodium iron-manganese phosphate–germanium nanowires,
has been developed and tested. The laboratory batteries possessed an average discharge voltage of ~2.0 V and an
energy density based on the weight of active materials to 210 Wh kg–1.

Research target: Materials Technologies Nanotechnologies Chemistry
Language: English
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DOI
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Keywords: энергоемкостьнанопроволокаSodium-ion batterygermaniumгерманийnanowireнатрий-ионный аккумуляторsodium iron-manganese phosphatecharge–discharge curvesenergy densityнатриевый фосфат железа-марганцакривые заряда-разряда
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