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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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Using Interface Patterns for Compositional Discovery of Distributed System Models

Proceedings of the Institute for System Programming of the RAS. 2017. Vol. 29. No. 4. P. 21–38.
R.A. Nesterov, I.A. Lomazova

Process mining offers various tools for studying process-aware information systems. They mainly involve several participants (or agents) managing and executing operations on the basis of process models. To reveal the actual behavior of agents, we can use process discovery. However, for large-scale processes, it does not yield models, which help understand how agents interact since they are independent and their concurrent implementation can lead to a very sophisticated behavior. To overcome this problem, we propose interface patterns, which allow getting models of multi-agent processes with a clearly identified agent behavior and interaction scheme as well. The correctness of patterns is provided via morphisms. We also conduct a preliminary experiment, results of which are highly competitive compared to the process discovery without interface patterns.

Priority areas: IT and mathematics
Language: English
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Keywords: распределенные системыкомпозициямультиагентные системысети ПетриPetri netsmulti-agent systemscompositionсинхронизацияprocess discoverySynchronization distributed systemsизвлечение процессовinterface patternsmorphismsпаттерны интерфейсовморфизмы
Publication based on the results of:
Synthesis and analysis of process models (2017)
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