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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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?

Constrained Multi-objective Robot’s Design Optimization

P. 1992–1995.
Posypkin M., Maminov A.

In this article, we consider an approach for the
optimal multi-objective constrained design of the parallel robots.
Optimization is carried out according to two key criteria: the
workspace area and the global dexterity index (GDI) concerning
constrain. The workspace is the area that the robot can serve.
The value of the GDI correlates with the quality of this service.
Both objectives are maximized. However, there is a tradeoff
between these goals, and they can't achieve maximums at the
same point. Therefore, the solution for this problem is a Pareto-optimal frontier (the set of points, which can’t be optimized in any criteria without worsening the other). The paper describes
the robot’s kinematic and singularity analysis, workspace area
approximation, the GDI calculation. The Pareto-frontier for the
multi-objective constrained optimization problem under
consideration is constructed. We also create representative
visualization for robot workspace, singularity positions, and
efficient Pareto-set

Language: English
Full text
DOI
Text on another site
Keywords: singularityglobal dexterity index multi-objective optimization

In book

IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW) 2020
IEEE, 2020.
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