|Table of Contents|

Intelligent detection method of liquid rocket engine health state(PDF)

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

Issue:
2021年04期
Page:
52-58
Research Field:
研究与设计
Publishing date:

Info

Title:
Intelligent detection method of liquid rocket engine health state
Author(s):
WANG Jun1; LV Haixin2; CHEN Jinglong2; LIU Zijun1; YUAN Junshe1
(1.Xian Aerospace Propulsion Institute,Xian 710100,China 2.State Key Laboratory of Manufacturing and Systems Engineering,Xian Jiaotong University,Xian 710049,China)
Keywords:
health state detection liquid rocket engine multi-sensor data convolutional autoencoder unsupervised learning
PACS:
V434,TP391
DOI:
-
Abstract:
The health state detection technology of liquid rocket engine is very important to improve the safety and reliability of rockets,which has important academic research and application value.The current research methods are mostly based on feature extraction and expert experience,while the research on intelligent detection technology needs to be improved.In this paper,Multi-component Two-stage Fusion Convolutional Denoising Auto-Encoder(MTAE)for intelligent detection of liquid rocket engine health state was proposed.First,unsupervised feature extraction and reconstruction of multi-sensor monitoring data were performed.After completing the learning of training set with the MTAE,the center of health and the monitoring threshold were determined based on the hidden layer features and reconstruction errors.Then,the state of each group of the testing set would be identified.In addition,the health state of each component could be further determined by calculating the loss of each component.Finally,the proposed method was verified by the data collected from the static firing test process.The method achieves an accuracy of 88.9% on the test set and could monitor the health and degradation trends of the whole engine and each component.The results show the effectiveness and application potential of the proposed method.

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