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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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?

Improving the Inventory Management Efficiency Based on the Fill Rate Assessment and the Safety Stock Level

P. 327–337.
Lukinskiy V., Lukinsky V., Bazhina D., Zolotukhina I., Gazizova E.

To enhance the efficiency of supply chain management, it is essen tial to reduce safety stock levels without compromising product availability. To achieve this objective, it was proposed to evaluate effectiveness of managerial decisions based on fill rate calculations. Fill rate is a common measure of the cus tomer service performance of inventory and is often presented as the percentage of units available when requested by the customer. The findings from several studies remain contentious; for instance, increasing the order quantity may offset a reduc tion in safety stock levels, or vice versa. Furthermore, our research reveals that existing approaches occasionally produce inexplicable outcomes, such as nega tive values for the fill rate or safety stock levels. The proposed approach adjusts existing methods based on the consideration of inventory management strategy with periodic or constant control period and proposes a methodology that allows managers to control the variable level of safety stock to maintain the desired value of the fill rate.

Language: English
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Keywords: управление цепями поставокстраховые запасы supply chain managementsafety stockFill Rate

In book

Reliability and Statistics in Transportation and Communication: Ecosystems for Smart Connectivity and Intelligent Mobility. Selected Papers from the 25th International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication, RelStat-2025, October 16–18, 2025, Riga, Latvia
Issue 1822. , Switzerland: Springer Publishing Company, 2026.
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