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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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Формирование теории и методологии управления цепями поставок трубопроводного транспорта

Логистика и управление цепями поставок. 2018. № 6(89). С. 112–128.
Plotnikov V., Сулейманова К. П.

The current situation in the world economy has a significant impact on the imbalance in the use of different types of natural resources. International energy supply chains are a crucial element of energy security, and their sustainability affects market conditions in most countries. The primary purpose of this article is to form the foundations of the theory and methodology of supply chain management of pipeline transport. Authors identified opportunities for the application of the method of the logistics to pipeline transport systems. By analyzing the features of pipeline transport in general, the authors propose the use of methods and models of logistics and supply chain management in the planning of the oil and gas transportation systems, taking into account the characteristics of flows while using this type of transport.

Research target: Economics and Management
Priority areas: management
Language: Russian
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Text on another site
Keywords: логистикацепи поставокметодологияmethodologyтрубопроводный транспортsupply chains logisticspipeline transport
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