|Table of Contents|

Intelligent anomaly detection of liquid rocket engine with multi-source data(PDF)

《火箭推进》[ISSN:1672-9374/CN:CN 61-1436/V]

Issue:
2022年03期
Page:
79-86
Research Field:
测控与试验
Publishing date:

Info

Title:
Intelligent anomaly detection of liquid rocket engine with multi-source data
Author(s):
LIU Zijun1;  FENG Yong2;  CHEN Jinglong2;  WANG Jun1;  ZHANG Zhiwei1
(1.Xian Aerospace Propulsion Institute, Xian 710100, China 2.State Key Laboratory of Manufacturing and Systems Engineering, Xian Jiaotong University, Xian 710049, China)
Keywords:
liquid rocket engine anomaly detection autoencoding generative adversarial network multi-source data
PACS:
V434,TP391
DOI:
-
Abstract:
To improve the reliability and safety of the space launch mission, the intelligent anomaly detection technology of liquid rocket engine was studied.Aiming at the problems of traditional methods such as low detection accuracy, relying on expert experience and prior knowledge, and large amount of data needed, a deep learning was proposed to build an autoencoding generative adversarial network to train the multi-source data under the engine health state.The reconstruction loss and discriminant score of input data were tested, and the intelligent anomaly detection of liquid rocket engine was completed.The analysis results of the ground hot test data for a liquid rocket engine show that this method achieves 96.55 test accuracy, and the 100 accuracy can be obtained by using the neighboring information.It can be effectively used for the anomaly detection of liquid rocket engine.

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