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

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...

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Bibliografische gegevens
Hoofdauteurs: O. F. Mashoshin, H. Huseynov, A. S. Zasukhin
Formaat: Artikel
Taal:Russisch
Gepubliceerd in: Moscow State Technical University of Civil Aviation 2024-12-01
Reeks:Научный вестник МГТУ ГА
Onderwerpen:
flight safety
gas turbine engine diagnostics
recurrent neural networks
long short-term memory
parameter forecasting
vibration
compressor
turbine
bptt algorithm
Online toegang:https://avia.mstuca.ru/jour/article/view/2465
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