Aeroengine Turbine Exhaust Gas Temperature Prediction Using Support Vector Machines

Authors:
Xuyun Fu;Gang Ding;Shisheng Zhong
Affiliations:
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China 150001;School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China 150001;School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China 150001
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

The turbine exhaust gas temperature (EGT) is an important parameter of the aeroengine and it represents the thermal health condition of the aeroengine. By predicting the EGT, the performance deterioration of the aeroengine can be deduced in advance. Thus, the flight safety and the economy of the airlines can be guaranteed. However, the EGT is influenced by many complicated factors during the practical operation of the aeroengine. It is difficult to predict the change tendency of the EGT effectively by the traditional methods. To solve this problem, a novel EGT prediction method based on the support vector machines (SVM) is proposed. Finally, the proposed prediction method is utilized to predict the EGT of some aeroengine, and the results are satisfying.