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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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Concept of Quantitative Assessment of Knowledge Uncertainty in Accident Reconstruction Tasks

P. 659–664.
Vasiliev Y., Evtiukov S., Lukinskiy V.

The article substantiates the concept of quantitative assessment of knowledge uncertainty in accident reconstruction tasks based on application of mathematical tools of the fuzzy set theory allowing considering an uncertainty of initial data caused for instance by varying resistance to the motion of investigated objects at the apex stages of the contact–separation processes. The application of the mathematical tools of the fuzzy set theory can substantially expand the potential of applying the methodology to the automobile and technical expertise and provide the enhancement of authenticity and improve the accuracy of making conclusions about the accident reconstruction results.

Language: English
DOI
Text on another site
Keywords: reconstructionaccidentdeformationfuzzy setvehiclecollisiontraffic situation

In book

19th EURO Working Group on Transportation Meeting, EWGT2016, 5-7 September 2016, Istanbul, Turkey
Т. 22. , Амстердам: Elsevier, 2017.
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