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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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Nonparametric statistical inference for compound models

Statistics. 2024. Vol. 58. No. 4. P. 943–960.
Belomestny D., Morozova E., Panov V.

This paper deals with the statistical inference in compound models, which are defined as a sum of i.i.d. random variables \(\xi_1+...+\xi_N\), where the number of summands, \(N\), is a random variable independent of \(\xi_1, \xi_2,...\) Using the novel technique based on the superposition of the Mellin and Laplace transforms, we construct a nonparametric estimator for the distribution of \(N\), assuming that  the distribution of \(\xi\) is known explicitly.  Unlike most papers on this topic, we consider the general setting, where the distribution of \(N\) is not necessarily of Poisson type.

Research target: Mathematics
Language: English
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DOI
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Keywords: Mellin transformпреобразование Меллинаdecompoundingcompound modelsсоставные моделидекомпаундинг
Publication based on the results of:
Development and theoretical analysis of new effective stochastic machine learning algorithms (2024)
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