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Instruments and Systems: Monitoring, Control, and Diagnostics

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Data Processing Algorithms for Diagnosing Electromechanical Systems Using Machine Learning
G.S. VERESNIKOV, A.V. SKRYABIN, A.V. GOLEV

The article discusses the creating algorithms problem for technical condition identifi cation of electromechanical systems (ES) using
machine learning methods. The problem urgency is due to the need for effectively using signifi cant amounts of empirical data that generated
in the operation and research of ES. Machine learning methods make it possible to synthesize algorithms for creating learning early diagnosis
systems that can adapt when new information arrives. To improve the effi ciency of machine learning methods, the work proposes data
processing schemes, models and algorithms for automated identifi cation of diagnostic features when constructing classifi cation models.
We presented the computational studies results using the technical condition-diagnosing example for electromechanical drive (EMD) when
processing a stationary control signal.
Keywords: diagnostics, electromechanical systems, machine learning, informative features, neural networks.


DOI: 10.25791/pribor.4.2024.1492

Pp. 40-48.

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