Methodology for diagnosing the technical condition of aviation gas turbine engines using recurrent neural networks (RNN) and long short-term memory networks (LSTM)
This study presents a method for diagnosing the technical condition of aviation gas turbine engines (GTE) using recurrent neural networks (RNN) and long short-term memory networks (LSTM). The primary focus is on comparing the effectiveness of these models for forecasting key operating parameters of...
Saved in:
Main Authors: | O. F. Mashoshin, H. Huseynov, A. S. Zasukhin |
---|---|
Format: | Article |
Language: | Russian |
Published: |
Moscow State Technical University of Civil Aviation
2024-12-01
|
Series: | Научный вестник МГТУ ГА |
Subjects: | |
Online Access: | https://avia.mstuca.ru/jour/article/view/2465 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Simulation of Coupled Heat Transfer in Rotor/Stator Cavity of the Microturbine
by: Volkov K.N., et al.
Published: (2019-12-01) -
EXPERIMENTAL STUDY OF NATURAL FORMS AND THE VIBRATION FREQUENCIES OF THE COMPRESSOR BLADES OF AIRCRAFT AUXULARY POWER UNIT
by: M. G. Belousov, et al.
Published: (2018-08-01) -
Identification of temporal anomalies of spectrograms of vibration measurements of a turbine generator rotor using a recurrent neural network autoencoder
by: V. P. Kulagin, et al.
Published: (2021-04-01) -
Performance estimation of a steam-turbine driven multistage compressor system
by: Aleksa Miladinović, et al.
Published: (2025-10-01) -
Theory and design of steam and gas turbines /
by: Lee, John F.
Published: (1954)