An Adaptive-Tunable-Based Hybrid RBF Network for EGTM Prediction
| Autor: | Hanlin Sheng, Yanyun Tian, Yuan Liu, Yishou Wang, Xianping Zeng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
particle filter
General Computer Science 020208 electrical & electronic engineering General Engineering Process (computing) Aero-engine 02 engineering and technology computer.software_genre exhaust gas temperature margin prediction Margin (machine learning) hybrid RBF network Linear regression 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General Materials Science Radial basis function Data mining lcsh:Electrical engineering. Electronics. Nuclear engineering Brownian motion Particle filter computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 9, Pp 19674-19681 (2021) |
| ISSN: | 2169-3536 |
| Popis: | Aero-engine exhaust gas temperature margin (EGTM) is one of the main indexes of engine replacement; however, the application of existing methods in EGTM forecasting is restricted because of the limited prediction accuracy and many non-linearities. In this study, an adaptive-tunable-based hybrid radial basis function (RBF) network is proposed to improve the prediction accuracy of aero-engine EGTM. Firstly, a hybrid RBF network consisting of a RBF network and a linear regression model is built as a fundamental EGTM predictive algorithm. Secondly, to increase the network’s adaptation capabilities, the structural parameters of the proposed network are adaptively modulated by Brownian motion modeling and particle filter without physics-based models. Finally, multiple sets of EGTM data from a certain type aero-engines in an airline company is selected for engine removal time prediction. Experiment results demonstrate that the proposed adaptive-tunable-based hybrid RBF network with a high prediction accuracy, and can reflect the characteristics of EGTM well and truly, which can capture the dynamic nature of EGTM in time during the forecasting process. |
| Databáze: | OpenAIRE |
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