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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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Spatially resolved dynamic structure factor of finite systems from molecular dynamics simulations

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics. 2011. Vol. 84. P. 036406.
Raitza T., Roepke G., Reinholz H., Morozov I.

The dynamical response of metallic clusters up to 103 atoms is investigated using the restricted molecular dynamics simulations scheme. Exemplarily, a sodium like material is considered. Correlation functions are evaluated to investigate the spatial structure of collective electron excitations and the optical response of laserexcited clusters. In particular, the spectrum of bilocal correlation functions shows resonances representing different modes of collective excitations inside the nano plasma. The spatial structure, the resonance energy, and the width of the eigenmodes have been investigated for various values of electron density, temperature, cluster size, and ionization degree. Comparison with bulk properties is performed and the dispersion relation of collective excitations is discussed.

Research target: Physics
Priority areas: engineering science
Language: English
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Keywords: molecular dynamicsnanoclusters
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