Aeroengine turbine exhaust gas temperature prediction using process support vector machines

Authors:
Xu-yun Fu;Shi-sheng Zhong
Affiliations:
School of Naval Architecture and Ocean Engineering, Harbin Institute of Technology at Weihai, Weihai, China;School of Naval Architecture and Ocean Engineering, Harbin Institute of Technology at Weihai, Weihai, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

Citing 4
Cited 0

Multilayer feedforward networks are universal approximators

Neural Networks
On the approximate realization of continuous mappings by neural networks

Neural Networks
The nature of statistical learning theory

The nature of statistical learning theory
Neural networks to predict earthquakes in Chile

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

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 and its remaining time-on-wing can be estimated. 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 named process support vector machine (PSVM) is proposed. The solving process of the PSVM, the kernel functional construction and its parameter optimization are also investigated. Finally, the proposed prediction method is utilized to predict the EGT of some aeroengine, and the results are satisfying.