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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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Эллиптические уравнения с разрывным коэффициентом. Компактная разностная схема для сложной границы сред

Гл. 10. С. 53–57.
Гордин В.А., Шадрин Д. А.

The boundary value problem for the Poisson and Helmholtz equations with a piecewise constant coefficient with a jump on a triangle is studied numerically. At the jump of the coefficient (at the boundary of the media), the docking conditions are set. A compact difference scheme with high accuracy with a relatively small number of calculations is proposed.

Language: Russian
Full text
Keywords: compact schemeкомпактная схемаPoisson equationуравнение ПуассонаHelmholtz equationjump of the coefficientboundary of the mediabroken boundary lineуравнение Гельмгольцаскачок коэффициентаграница средломаная линия границы
Publication based on the results of:
Математические модели. Дифференциальные уравнения и большие массивы информации. Аналитические и вычислительные методы. Практические приложения (2018)

In book

Современные проблемы математического моделирования: сборник трудов XVIII Всероссийской конференции-школы молодых исследователей (пос. Абрау-Дюрсо, 16–20 сентября 2019 г.)
Ростов н/Д: Издательство ЮФУ, 2019.
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