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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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Searching for Deviations in Trading Systems: Combining Control-Flow and Data Perspectives

P. 94–106.
Julio C. Carrasquel, Irina A. Lomazova

Trading systems are software platforms that support the exchange of securities (e.g., company shares) between participants. In this paper, we present a method to search for deviations in trading systems by checking conformance between colored Petri nets and event logs. Colored Petri nets (CPNs) are an extension of Petri nets, a formalism for modeling of distributed systems. CPNs allow us to describe an expected causal ordering between system activities and how data attributes of domain-related objects (e.g., orders to trade) must be transformed. Event logs consist of traces corresponding to runs of a real system. By comparing CPNs and event logs, different types of deviations can be detected. Using this method, we report the validation of a real-life trading system.

Language: English
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Keywords: coloured Petri netsPetri netsprocess miningconformance checkingtrading systems
Publication based on the results of:
Digital Trace-Based Modeling and Analysis of Processes in Information Systems (2024)

In book

6th International Conference, TMPA 2021, Tomsk, Russia, November 25–27, 2021, Revised Selected Papers. Tools and Methods of Program Analysis
Vol. 1559: CCIS . , Springer, 2024.
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