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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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?

Digital Ecosystem-Based KPI-Driven Railway Communication Network Reporting System

P. 163–166.
Panfilov, P., Suleykin, A., ElDarawany, A.

This research is focused on architectural and modeling issues of design and development of digital reporting system aimed at the railway communication network infrastructure. Our approach to these problems is based on digital ecosystem paradigm and open-source Big Data technologies. It also aims at methodology for KPIs data preparation and collection in railway communication networks. A practical result of the research is a proposed software framework for digital reporting system that consists of multiple agents, which are important for efficient KPIs data integration and processing in communication network reporting system. We have created and tested a prototype digital reporting system using KPIs data from the production railway communication network. KPIs data used in test implementation and obtained from the largest Russian Railway company included all incidents, occurred in the railway communication network in a week period. Prototype system implementation successfully processed all KPIs data using proposed data methodology and integrated framework.

Language: English
DOI
Text on another site
Keywords: distributed systemsKPI datadigital ecosystemsrailway communication network reportingopen-source big data technologies

In book

MEDES '21: Proceedings of the 13th International Conference on Management of Digital EcoSystems
NY: Association for Computing Machinery (ACM), 2021.
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