On the experimental setup for approbation of an algorithm for processing diagnostic parameters of aircraft gas turbine engine based on multilayer neural networks
- Autores: Huseynov H.1, Mashoshin O.F.1
-
Afiliações:
- Moscow State Technical University of Civil Aviation
- Edição: Nº 3 (2025)
- Páginas: 71-86
- Seção: Aircraft, aircraft engines and methods of their operation
- URL: https://ogarev-online.ru/2312-1327/article/view/360035
- DOI: https://doi.org/10.51955/2312-1327_2025_3_71
- ID: 360035
Citar
Texto integral
Resumo
The paper presents experimentally substantiated tabular data for hyperparameter tuning of multilayer neural networks in aviation gas turbine engine diagnostics. The authors propose seven original algorithms for adaptive training parameter tuning, including methods for dynamic adaptation of the learning rate, strategies for changing the network architecture depending on the engine operating mode, and adaptive approaches to regularization. The parameter ranges cover values from 10–5 to 103, which ensures practical applicability for various architectures and data types. The scientific novelty lies in the creation of adaptive algorithms that take into account the specifics of the diagnostic parameters of gas turbine engine components and their time dynamics.
Sobre autores
H. Huseynov
Moscow State Technical University of Civil Aviation
Autor responsável pela correspondência
Email: khuseyn.21@gmail.com
ORCID ID: 0009-0002-9280-6361
Postgraduate student Moscow
O. Mashoshin
Moscow State Technical University of Civil Aviation
Email: o.mashoshin@mstuca.ru
ORCID ID: 0009-0004-8099-5198
Doctor of Technical Sciences, Professor Moscow
Bibliografia
- Pei X., Zheng X., Wu J. (2021). Rotating machinery fault diagnosis through a transformer convolution network subjected to transfer learning. IEEE Transactions on Instrumentation and Measurement. 70: 1-11.
- Sembiring J., Sasongko R. A., Bastian E. I., Raditya B. A., Limansubroto R. E. (2024). A deep learning approach for trajectory control of tilt-rotor UAV. Aerospace. 11(1): 96.
- Smith L. N. Cyclical learning rates for training neural networks. (2017). IEEE Winter Conference on Applications of Computer Vision. 464-472.
- Tahan M., Tsoutsanis E., Muhammad M., Karim Z. A. A. (2017). Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied Energy. 198: 122-144.
- Tsoutsanis E., Meskin N., Benammar M., Khorasani K. (2019). A review on gas turbine gas-path diagnostics: state-of-the-art methods, challenges and opportunities. Aerospace. 6(7): 83.
- Zhou D., Yao Q., Wu H., Ma S., Zhang H. (2020). Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks. Energy. 200. doi: 10.1016/j.energy.2020.117467.
Arquivos suplementares
