Deep neural network-based fault diagnosis approach for the rocket propelling nozzle in the glide

Kai Quan; Bing Xiao; Zhenzhou Fu; Jia Yang; Chaofan Wu; Yiyan Wei

doi:10.1109/GNCC42960.2018.9019062

Deep neural network-based fault diagnosis approach for the rocket propelling nozzle in the glide

Kai Quan
, Bing Xiao
, Zhenzhou Fu
, Jia Yang
, Chaofan Wu
, Yiyan Wei

Bohai University

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

Abstract

This paper presents an intelligent approach for fault diagnosis about rocket nozzle in glide phase. In contrast to the commonly used observer or any other technologies over the complicated rocket, the proposed approach is based on a deep neural network for processing fault problems without rocket mathematical model accurately. The intelligent method, based on a Dynamic neural network with cross-entropy lose function, automatically identifies the faults of rocket nozzle, effectively acting as a fault classifier. This approach provides a simple solution for modeling difficult problems and allows multi-classes faults to be recognized in a straightforward way.

Original language	English
Title of host publication	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538611715
DOIs	https://doi.org/10.1109/GNCC42960.2018.9019062
State	Published - Aug 2018
Externally published	Yes
Event	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 - Xiamen, China Duration: 10 Aug 2018 → 12 Aug 2018

Publication series

Name	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

Conference

Conference	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Country/Territory	China
City	Xiamen
Period	10/08/18 → 12/08/18

Keywords

Attitude control
Cross-entropy cost function
Dynamic neural network
Fault diagnosis

Access to Document

10.1109/GNCC42960.2018.9019062

Cite this

Quan, K., Xiao, B., Fu, Z., Yang, J., Wu, C., & Wei, Y. (2018). Deep neural network-based fault diagnosis approach for the rocket propelling nozzle in the glide. In 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 Article 9019062 (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GNCC42960.2018.9019062

@inproceedings{f6b844b7c34a4f7f8245dbdc964e18ce,

title = "Deep neural network-based fault diagnosis approach for the rocket propelling nozzle in the glide",

abstract = "This paper presents an intelligent approach for fault diagnosis about rocket nozzle in glide phase. In contrast to the commonly used observer or any other technologies over the complicated rocket, the proposed approach is based on a deep neural network for processing fault problems without rocket mathematical model accurately. The intelligent method, based on a Dynamic neural network with cross-entropy lose function, automatically identifies the faults of rocket nozzle, effectively acting as a fault classifier. This approach provides a simple solution for modeling difficult problems and allows multi-classes faults to be recognized in a straightforward way.",

keywords = "Attitude control, Cross-entropy cost function, Dynamic neural network, Fault diagnosis",

author = "Kai Quan and Bing Xiao and Zhenzhou Fu and Jia Yang and Chaofan Wu and Yiyan Wei",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 ; Conference date: 10-08-2018 Through 12-08-2018",

year = "2018",

month = aug,

doi = "10.1109/GNCC42960.2018.9019062",

language = "英语",

series = "2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018",

}

Quan, K, Xiao, B, Fu, Z, Yang, J, Wu, C & Wei, Y 2018, Deep neural network-based fault diagnosis approach for the rocket propelling nozzle in the glide. in 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018., 9019062, 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018, Xiamen, China, 10/08/18. https://doi.org/10.1109/GNCC42960.2018.9019062

Deep neural network-based fault diagnosis approach for the rocket propelling nozzle in the glide. / Quan, Kai; Xiao, Bing; Fu, Zhenzhou et al.
2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 9019062 (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018).

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

TY - GEN

T1 - Deep neural network-based fault diagnosis approach for the rocket propelling nozzle in the glide

AU - Quan, Kai

AU - Xiao, Bing

AU - Fu, Zhenzhou

AU - Yang, Jia

AU - Wu, Chaofan

AU - Wei, Yiyan

PY - 2018/8

Y1 - 2018/8

N2 - This paper presents an intelligent approach for fault diagnosis about rocket nozzle in glide phase. In contrast to the commonly used observer or any other technologies over the complicated rocket, the proposed approach is based on a deep neural network for processing fault problems without rocket mathematical model accurately. The intelligent method, based on a Dynamic neural network with cross-entropy lose function, automatically identifies the faults of rocket nozzle, effectively acting as a fault classifier. This approach provides a simple solution for modeling difficult problems and allows multi-classes faults to be recognized in a straightforward way.

AB - This paper presents an intelligent approach for fault diagnosis about rocket nozzle in glide phase. In contrast to the commonly used observer or any other technologies over the complicated rocket, the proposed approach is based on a deep neural network for processing fault problems without rocket mathematical model accurately. The intelligent method, based on a Dynamic neural network with cross-entropy lose function, automatically identifies the faults of rocket nozzle, effectively acting as a fault classifier. This approach provides a simple solution for modeling difficult problems and allows multi-classes faults to be recognized in a straightforward way.

KW - Attitude control

KW - Cross-entropy cost function

KW - Dynamic neural network

KW - Fault diagnosis

UR - https://www.scopus.com/pages/publications/85082441996

U2 - 10.1109/GNCC42960.2018.9019062

DO - 10.1109/GNCC42960.2018.9019062

M3 - 会议稿件

AN - SCOPUS:85082441996

T3 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

BT - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

Y2 - 10 August 2018 through 12 August 2018

ER -

Quan K, Xiao B, Fu Z, Yang J, Wu C, Wei Y. Deep neural network-based fault diagnosis approach for the rocket propelling nozzle in the glide. In 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 9019062. (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018). doi: 10.1109/GNCC42960.2018.9019062

Deep neural network-based fault diagnosis approach for the rocket propelling nozzle in the glide

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