数据驱动的液体火箭发动机健康监测算法研究

刘梓琰; 吴毅伟; 王冠; 李璨; 王姝淇; 陈海宝

doi:10.7652/xjtuxb202404017

您当前的位置：

首页 >

文章列表页 >

数据驱动的液体火箭发动机健康监测算法研究

—————————————————————— | 更新时间：2025-07-09

- 数据驱动的液体火箭发动机健康监测算法研究
- Data-Driven Research on Health Monitoring Algorithms for the Liquid Rocket Engine
- 西安交通大学学报 2024年58卷第4期页码：182-191
- 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：
  
  : 2023-11-13。作者简介: 刘梓琰(1994—),男,工程师
  陈海宝(通信作者),男,副教授,博士生导师。基金项目: 国防科技173计划技术领域基金资助项目(2021-JCJQ-JJ-1181)
- 基金信息：
  
  : 2023-11-13。作者简介: 刘梓琰(1994—),男,工程师;陈海宝(通信作者),男,副教授,博士生导师。基金项目: 国防科技173计划技术领域基金资助项目(2021-JCJQ-JJ-1181);上海航天先进技术联合研究中心资助项目(USCAST2019-24)
- DOI：10.7652/xjtuxb202404017
  中图分类号： V421.4
- 网络首发：2024-04-10，
  
  纸质出版：2024
- 稿件说明：
移动端阅览
刘梓琰, 吴毅伟, 王冠, 等. 数据驱动的液体火箭发动机健康监测算法研究[J]. 西安交通大学学报, 2024,58(4):182-191.

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.
刘梓琰, 吴毅伟, 王冠, 等. 数据驱动的液体火箭发动机健康监测算法研究[J]. 西安交通大学学报, 2024,58(4):182-191. DOI： 10.7652/xjtuxb202404017.

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

references

曾瑞林, 王冠, 王潇宇, 等. 火箭喷管极性自动化测试系统的改进设计研究 [J]. 宇航总体技术, 2022, 6(6): 52-61.

ZENG Ruilin, WANG Guan, WANG Xiaoyu, et al. Research on rocket nozzle polarity automatic identification system improvement [J]. Astronautical Systems Engineering Technology, 2022, 6(6): 52-61.

马俊添, 张素明, 阎小涛, 等. 基于图像识别的机械振动信号特征提取与寿命预测方法研究 [J]. 宇航总体技术, 2021, 5(4): 33-42.

MA Juntian, ZHANG Suming, YAN Xiaotao, et al. Research on feature extraction and remaining useful life prediction for mechanical vibration signal based on image recognition [J]. Astronautical Systems Engineering Technology, 2021, 5(4): 33-42.

陈曦, 廖明夫, 王俨剀. 航空发动机故障融合诊断研究 [J]. 航空发动机, 2013, 39(4): 78-84.

CHEN Xi, LIAO Mingfu, WANG Yankai. Investigation of aeroengine fault fusion diagnosis [J]. Aeroengine, 2013, 39(4): 78-84.

KONAR P, CHATTOPADHYAY P. Bearing fault detection of induction motor using wavelet and support vector machines [J]. Applied Soft Computing, 2011, 11(6): 4203-4211.

SHARMA R K, SUGUMARAN V, KUMAR H, et al. A comparative study of naive Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal [J]. International Journal of Decision Support Systems, 2015, 1(1): 115-129.

MBO'O C P, HAMEYER K. Fault diagnosis of bearing damage by means of the linear discriminant analysis of stator current features from the frequency selection [J]. IEEE Transactions on Industry Applications, 2016, 52(5): 3861-3868.

ZHAO Yongping, SONG Fangquan, PAN Yingting, et al. Retargeting extreme learning machines for classification and their applications to fault diagnosis of aircraft engine [J]. Aerospace Science and Technology, 2017, 71: 603-618.

LI Bing, ZHAO Yongping. Group reduced kernel extreme learning machine for fault diagnosis of aircraft engine [J]. Engineering Applications of Artificial Intelligence, 2020, 96: 103968.

PANDYA D H, UPADHYAY S H, HARSHA S P. Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN [J]. Expert Systems with Applications, 2013, 40(10): 4137-4145.

PAN Haiyang, YANG Yu, ZHENG Jinde, et al. A fault diagnosis approach for roller bearing based on symplectic geometry matrix machine [J]. Mechanism and Machine Theory, 2019, 140: 31-43.

SEERA M, WONG M L D, NANDI A K. Classification of ball bearing faults using a hybrid intelligent model [J]. Applied Soft Computing, 2017, 57: 427-435.

LI Xin, YANG Yu, PAN Haiyang, et al. A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis [J]. Computers in Industry, 2019, 110: 36-47.

XI Pengpeng, ZHAO Yongping, WANG Peixiao, et al. Least squares support vector machine for class imbalance learning and their applications to fault detection of aircraft engine [J]. Aerospace Science and Technology, 2019, 84: 56-74.

ZHAO Yongping, WANG Jianjun, LI Xiaoya, et al. Extended least squares support vector machine with applications to fault diagnosis of aircraft engine [J]. ISA Transactions, 2020, 97: 189-201.

YANG Yu, YU Dejie, CHENG Junsheng. A roller bearing fault diagnosis method based on EMD energy entropy and ANN [J]. Journal of Sound and Vibration, 2006, 294(1/2): 269-277.

HARMOUCHE J, DELPHA C, DIALLO D. Improved fault diagnosis of ball bearings based on the global spectrum of vibration signals [J]. IEEE Transactions on Energy Conversion, 2015, 30(1): 376-383.

RAI V K, MOHANTY A R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform [J]. Mechanical Systems and Signal Processing, 2007, 21(6): 2607-2615.

HE Miao, HE D. A new hybrid deep signal processing approach for bearing fault diagnosis using vibration signals [J]. Neurocomputing, 2020, 396: 542-555.

LI H, ZHOU P, MA X. Pattern recognition on diesel engine working condition by using a novel methodology: Hilbert spectrum entropy [J]. Journal of Marine Engineering Technology, 2005, 4(1): 43-48.

MATHEW S K, ZHANG Yu. Acoustic-based engine fault diagnosis using WPT, PCA and Bayesian optimization [J]. Applied Sciences, 2020, 10(19): 6890.

GEORGOULAS G, NIKOLAKOPOULOS G. Bearing fault detection and diagnosis by fusing vibration data [C]//IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society. Piscataway, NJ, USA: IEEE, 2016: 6955-6960.

薛华. 基于阶比分析的机动车变速箱齿轮故障检测方法研究 [J]. 中国机械, 2023(20): 83-86.

XUE Hua. Research on vehicle transmission gear fault detection method based on order analysis [J]. Machine China, 2023(20): 83-86.

何东中, 贡丽霞, 白艳萍. 小波变换和神经网络的电路故障诊断 [J]. 现代电子技术, 2020, 43(10): 30-32.

HE Dongzhong, GONG Lixia, BAI Yanping. Circuit fault diagnosis based on wavelet transform and neural network [J]. Modern Electronics Technique, 2020, 43(10): 30-32.[24] 张可, 崔乐. 基于PCA-LSTM模型的多元时间序列分类算法研究 [J]. 统计与决策, 2020, 36(15): 44-49.

ZHANG Ke, CUI Le. Research on multivariate time series classification algorithm based on PCA-LSTM model [J]. Statistics Decision, 2020, 36(15): 44-49.

卢海涛, 王自力. 综合航空电子系统故障诊断与健康管理技术发展 [J]. 电光与控制, 2015, 22(8): 60-65.

LU Haitao, WANG Zili. Development review of integrated avionics prognostics and health management technology [J]. Electronics Optics Control, 2015, 22(8): 60-65.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

航空发动机高温结构损伤检测研究与应用进展

采用似然比检测的电动车锂离子电池传感器故障检测方法

多运营工况下高速列车轴箱轴承的阈值自适配智能健康监测方法

采用似然比检测的电动车锂离子电池传感器故障检测方法

多工况下离心压气机叶轮-机匣一体联合设计优化