Data-Driven Research on Health Monitoring Algorithms for the Liquid Rocket Engine

LIU Ziyan; WU Yiwei; WANG Guan; LI Can; WANG Shuqi; CHEN Haibao

doi:10.7652/xjtuxb202404017

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Data-Driven Research on Health Monitoring Algorithms for the Liquid Rocket Engine

更新时间：2025-07-09

- Data-Driven Research on Health Monitoring Algorithms for the Liquid Rocket Engine
- Vol. 58, Issue 4, Pages: 182-191(2024)
- 作者机构：
  
  1. 北京宇航系统工程研究所,北京,100076
  2. 上海交通大学电子信息与电气工程学院,上海,200240
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202404017
  CLC： V421.4
- Online First：10 April 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
LIU Ziyan, WU Yiwei, WANG Guan, et al. Data-Driven Research on Health Monitoring Algorithms for the Liquid Rocket Engine[J]. 2024, 58(4): 182-191.
DOI：

LIU Ziyan, WU Yiwei, WANG Guan, et al. Data-Driven Research on Health Monitoring Algorithms for the Liquid Rocket Engine[J]. 2024, 58(4): 182-191. DOI： 10.7652/xjtuxb202404017.

摘要

针对火箭发动机氧涡轮转子轴盘根部断裂、轴盘接合处裂纹等故障难以识别定位的问题

基于某型液体火箭发动机测试数据

构建机器学习、数据分析等方法

绕过其内部复杂物理机理

从数据层面对氧涡轮泵进行故障检测与模式判别。对于故障检测

提出了两种分别适用于速变数据与缓变数据的故障检测算法

两种算法能够处理多种工况下的火箭发动机数据

经检验

这两种算法都具有较高的准确率

分别为84.2%、94.9%。针对故障模式判别

给出了一种基于滑动窗口的聚类算法

可以实现不同故障模式的区分

其中针对两种故障模式的识别准确率分别达到了86.2%、95.5%

并且给出了两种故障模式对应的振动数据异常频率区间

这可为相关研究人员提供与故障有关的有用线索。

Abstract

Regarding the difficulty in identifying and locating faults such as the root fracture of the oxygen rotor of the rocket engine and cracks at the joint of the shaft disk

fault detection and mode discrimination at the data level are conducted based on the test data of a certain liquid rocket engine through machine learning

data analysis

and other methods while bypassing the internal complex physical mechanism. For fault detection

two fault detection algorithms respectively applicable to fast-variable data and slow-variable data are proposed

which can process rocket engine data under various working conditions and achieve high accuracy rates of 84.2% and 94.9% respectively after testing. For fault mode discrimination

a clustering algorithm based on the sliding window is presented

which can realize the distinction of different fault modes

and the recognition accuracy of the two fault modes can reach 86.2% and 95.5% respectively. The abnormal frequency interval of vibration data corresponding to the two fault modes is given

thus providing related clues for relevant researchers.

关键词

Keywords

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