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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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?

CubicEoS.jl: Extensible, Open-Source Isothermal Phase Equilibrium Calculations for Fluids

P. 59–73.
Zakharov S., Pisarev V.

We present open-source software for isochoric isothermal phase equilibrium calculation of fluids extensible on custom equations of state. We demonstrate robustness of the solvers by calculation of binodals and equilibrium parameters for a number of mixtures modelled by a cubic and a SAFT-family equations of state. Additionally, we consider multi-threaded computation of a phase diagram. The solvers are based on quasi-Newton minimization of Helmholtz free energy. The software is written in Julia language, and we discuss language features making it favorable for computational thermophysics problems.

Language: English
DOI
Text on another site
Keywords: phase equilibrianumerical optimizationparallel multithread computations

In book

Supercomputing: 9th Russian Supercomputing Days, RuSCDays 2023, Moscow, Russia, September 25–26, 2023, Revised Selected Papers, Part I
Springer, 2023.
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