Aeroengine turbine exhaust gas temperature prediction using support vector machines

Xuyun Fu; Gang Ding; Shisheng Zhong

doi:10.1007/978-3-642-01510-6_27

Aeroengine turbine exhaust gas temperature prediction using support vector machines

Xuyun Fu^*
, Gang Ding
, Shisheng Zhong

^*Corresponding author for this work

School of Mechatronics Engineering, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

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.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings
Pages	235-241
Number of pages	7
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-01510-6_27
State	Published - 2009
Externally published	Yes
Event	6th International Symposium on Neural Networks, ISNN 2009 - Wuhan, China Duration: 26 May 2009 → 29 May 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	5552 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Symposium on Neural Networks, ISNN 2009
Country/Territory	China
City	Wuhan
Period	26/05/09 → 29/05/09

Keywords

Aeroengine condition monitoring
Support vector machines
Time series prediction
Turbine exhaust gas temperature

Access to Document

10.1007/978-3-642-01510-6_27

Cite this

Fu, X., Ding, G., & Zhong, S. (2009). Aeroengine turbine exhaust gas temperature prediction using support vector machines. In Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings (PART 2 ed., pp. 235-241). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5552 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-01510-6_27

Fu, Xuyun ; Ding, Gang ; Zhong, Shisheng. / Aeroengine turbine exhaust gas temperature prediction using support vector machines. Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings. PART 2. ed. 2009. pp. 235-241 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{3ced310024bd409083e79eea68cd1a2f,

title = "Aeroengine turbine exhaust gas temperature prediction using support vector machines",

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.",

keywords = "Aeroengine condition monitoring, Support vector machines, Time series prediction, Turbine exhaust gas temperature",

author = "Xuyun Fu and Gang Ding and Shisheng Zhong",

year = "2009",

doi = "10.1007/978-3-642-01510-6\_27",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings",

edition = "PART 2",

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Fu, X , Ding, G & Zhong, S 2009, Aeroengine turbine exhaust gas temperature prediction using support vector machines. in Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5552 LNCS, pp. 235-241, 6th International Symposium on Neural Networks, ISNN 2009, Wuhan, China, 26/05/09. https://doi.org/10.1007/978-3-642-01510-6_27

Aeroengine turbine exhaust gas temperature prediction using support vector machines. / Fu, Xuyun ; Ding, Gang ; Zhong, Shisheng.
Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings. PART 2. ed. 2009. p. 235-241 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5552 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Aeroengine turbine exhaust gas temperature prediction using support vector machines

AU - Fu, Xuyun

AU - Ding, Gang

AU - Zhong, Shisheng

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Aeroengine condition monitoring

KW - Support vector machines

KW - Time series prediction

KW - Turbine exhaust gas temperature

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DO - 10.1007/978-3-642-01510-6_27

M3 - 会议稿件

AN - SCOPUS:69849087885

SN - 3642015093

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Fu X , Ding G , Zhong S. Aeroengine turbine exhaust gas temperature prediction using support vector machines. In Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings. PART 2 ed. 2009. p. 235-241. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-01510-6_27

Aeroengine turbine exhaust gas temperature prediction using support vector machines

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Keywords

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