Methodology for diagnosing the technical condition of aviation gas turbine engines using recurrent neural networks (RNN) and long short-term memory networks (LSTM) | Mashoshin | Civil Aviation High Technologies

Civil Aviation High Technologies
Nauchnyi Vestnik MGTU GA

ISSN 2079-0619 (Print)
ISSN 2542-0119 (Online)

eng | рус

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Mashoshin O.F., Huseynov H., Zasukhin A.S. Methodology for diagnosing the technical condition of aviation gas turbine engines using recurrent neural networks (RNN) and long short-term memory networks (LSTM). Civil Aviation High Technologies. 2024;27(6):21-41. (In Russ.) https://doi.org/10.26467/2079-0619-2024-27-6-21-41

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ISSN 2079-0619 (Print)
ISSN 2542-0119 (Online)

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Eliseev B.P.

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About the Authors

O. F. Mashoshin
Moscow State Technical University of Civil Aviation
Russian Federation

Oleg F. Mashoshin, Doctor of Technical Sciences, Professor, The Head of the Aircraft Engines Chair

Moscow

H. Huseynov
Moscow State Technical University of Civil Aviation
Russian Federation

Huseyn Huseynov, Postgraduate Student, Aircraft Engines Chair

Moscow

A. S. Zasukhin
Moscow State Technical University of Civil Aviation
Russian Federation

Aleksandr S. Zasukhin, The Head of the Training and Simulator Center, Senior Lecturer, Aircraft Engines Chair

Moscow

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