An Adaptive-Tunable-Based Hybrid RBF Network for EGTM Prediction

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 radi...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.19674-19681
Main Authors: Liu, Yuan, Zeng, Xianping, Tian, Yanyun, Wang, Yishou, Sheng, Hanlin
Format: Article
Language:English
Subjects:
Accuracy
Adaptation models
Aero-engine
Aerospace engines
Algorithms
Biological neural networks
Brownian motion
Engines
exhaust gas temperature margin prediction
Exhaust gases
Forecasting
Gas temperature
hybrid RBF network
Mathematical model
particle filter
Predictive models
Radial basis function
Radial basis function networks
Regression models
Structural engineering
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3052465