Aeroengine turbine exhaust gas temperature prediction using process support vector machines

Xu Yun Fu; Shi Sheng Zhong

doi:10.1007/978-3-642-39065-4_37

Aeroengine turbine exhaust gas temperature prediction using process support vector machines

Harbin Institute of Technology Weihai

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

8 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 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.

Original language	English
Title of host publication	Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings
Publisher	Springer Verlag
Pages	300-310
Number of pages	11
Edition	PART 1
ISBN (Print)	9783642390647
DOIs	https://doi.org/10.1007/978-3-642-39065-4_37
State	Published - 2013
Externally published	Yes
Event	10th International Symposium on Neural Networks, ISNN 2013 - Dalian, China Duration: 4 Jul 2013 → 6 Jul 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7951 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Symposium on Neural Networks, ISNN 2013
Country/Territory	China
City	Dalian
Period	4/07/13 → 6/07/13

Keywords

Aeroengine
Condition monitoring
Process support vector machines
Time series prediction
Turbine exhaust gas temperature

Access to Document

10.1007/978-3-642-39065-4_37

Cite this

Fu, X. Y., & Zhong, S. S. (2013). Aeroengine turbine exhaust gas temperature prediction using process support vector machines. In Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings (PART 1 ed., pp. 300-310). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7951 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-642-39065-4_37

Fu, Xu Yun ; Zhong, Shi Sheng. / Aeroengine turbine exhaust gas temperature prediction using process support vector machines. Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 1. ed. Springer Verlag, 2013. pp. 300-310 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Aeroengine turbine exhaust gas temperature prediction using process 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 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.",

keywords = "Aeroengine, Condition monitoring, Process support vector machines, Time series prediction, Turbine exhaust gas temperature",

author = "Fu, \{Xu Yun\} and Zhong, \{Shi Sheng\}",

year = "2013",

doi = "10.1007/978-3-642-39065-4\_37",

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Fu, XY & Zhong, SS 2013, Aeroengine turbine exhaust gas temperature prediction using process support vector machines. in Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7951 LNCS, Springer Verlag, pp. 300-310, 10th International Symposium on Neural Networks, ISNN 2013, Dalian, China, 4/07/13. https://doi.org/10.1007/978-3-642-39065-4_37

Aeroengine turbine exhaust gas temperature prediction using process support vector machines. / Fu, Xu Yun ; Zhong, Shi Sheng.
Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 1. ed. Springer Verlag, 2013. p. 300-310 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7951 LNCS, No. PART 1).

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

TY - GEN

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

AU - Fu, Xu Yun

AU - Zhong, Shi Sheng

PY - 2013

Y1 - 2013

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 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.

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 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.

KW - Aeroengine

KW - Condition monitoring

KW - Process support vector machines

KW - Time series prediction

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Fu XY , Zhong SS. Aeroengine turbine exhaust gas temperature prediction using process support vector machines. In Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 1 ed. Springer Verlag. 2013. p. 300-310. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-39065-4_37

Aeroengine turbine exhaust gas temperature prediction using process support vector machines

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