基于AGABP神经网络的运载火箭推力偏移损失故障在线诊断

doi:10.7527/S1000-6893.2024.31148

Abstract

Abstract:

To address the thrust deviation loss fault in the launch vehicle’s power system， an online detection and diagnosis method for thrust faults based on the Adaptive Genetic Algorithm-based Back Propagation （AGABP） neural network is proposed. To achieve low-latency， high-precision online detection and diagnosis of thrust loss faults， this method solely utilizes the rocket motion information measured by onboard sensors. Firstly， a six-degree-of-freedom （6-DOF） modeling is established based on the data and thrust fault types of a certain type of launch vehicle in China. Historical state information sensitive to faults， such as overload and apparent acceleration， was used as inputs for network training. Secondly， the initial weights in the BP neural network are adjusted through the adaptive genetic algorithm to obtain optimized network parameters. Finally， the resulting online diagnostic model for thrust deviation loss faults in launch vehicles is verified through 6-DOF online simulations. Numerical simulation results indicate that compared with the traditional BP network， the AGABP-based method exhibits faster convergence speed with fewer iteration generations. The accuracy of fault location is 96.51%， the fault location delay is between 0.1 s and 2 s， and the difference between the predicted and actual thrust reduction degree is within 20% for 94.19% of the samples.

Key words: launch vehicle, power system fault, fault detection and diagnosis, adaptive genetic algorithm, neural network

CLC Number:

V434.3

Haipeng CHEN, Wenxing FU, Jie YAN. Fault diagnosis of thrust offset loss of launch vehicle based on AGABP neural network[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(8): 231148.

Figures/Tables 16

Fig.1

Fig.2

Fig.3

Table 1

Fault flag bit data form

故障发动机编号		1	2	3	4
无故障段	10.00 s	0	0	0	0
推力下降段	11.00 s	0	0	0.5	0
	12.00 s	0	0	0.5	0
	$⋮$	$⋮$	$⋮$	$⋮$	$⋮$
	16.00 s	0	0	0.5	0
故障推力保持段	17.00 s	0	0	0.5	0
	$⋮$	$⋮$	$⋮$	$⋮$	$⋮$
	20.00 s	0	0	0.5	0

Table 1

Fig.4

Table 2

Fig.5

Table 3

Table 4

Test results of accuracy estimation of thrust fault loss degree

$e c$ /%	满足范围的次数/次	准确率/%
只要求检测出故障	86	100
20	81	94.19
15	69	80.23
10	54	62.79

Table 4

Table 5

Fig.6

Fig.7

Fig.8

Fig.9

Table 6

Table 7

Comparison result of accuracy of fault loss degree detection

$e c$ /%	$e l o s s$ /%
$e c$ /%	BP神经网络	AGABP神经网络
只要求检测出故障	100	100
30	97.46	100
20	89.84	93.33
15	64.76	87.62

Table 7

References 31

1	张荣升，吴燕生，秦旭东，等. 运载火箭推力下降故障下的在线弹道重构方法［J］. 南京航空航天大学学报， 2021， 53（）： 25-31.
	ZHANG R S， WU Y S， QIN X D， et al. Online trajectory reconstruction method for launch vehicle under thrust degradation fault［J］. Journal of Nanjing University of Aeronautics & Astronautics， 2021， 53（Sup 1）： 25-31 （in Chinese）.
2	张振臻，陈晖，高玉闪，等. 液体火箭发动机故障诊断技术综述［J］. 推进技术， 2022， 43（6）： 20-38.
	ZHANG Z Z， CHEN H， GAO Y S， et al. Review on fault diagnosis technology of liquid rocket engine‍［J］. Journal of Propulsion Technology， 2022， 43（6）： 20-38 （in Chinese）.
3	PARK S Y， AHN J. Deep neural network approach for fault detection and diagnosis during startup transient of liquid-propellant rocket engine‍［J］. Acta Astronautica， 2020， 177： 714-730.
4	张振臻，陈晖，高玉闪. 基于滑动时间窗主成分分析的液体火箭发动机传感器故障诊断方法［J］. 推进技术， 2022， 43（9）： 343-353.
	ZHANG Z Z， CHEN H， GAO Y S. Sliding time windows principal component analysis based fault diagnosis method for liquid rocket engine sensors‍［J］. Journal of Propulsion Technology， 2022， 43（9）： 343-353 （in Chinese）.
5	胡海峰，王晋麟，黄聪，等. 运载火箭非致命故障下弹道规划制导和自适应控制重构技术［J］. 载人航天， 2022， 28（4）： 439-448.
	HU H F， WANG J L， HUANG C， et al. Trajectory planning guidance and adaptive reconfiguration control of launch vehicle under non-fatal failure［J］. Manned Spaceflight， 2022， 28（4）： 439-448 （in Chinese）.
6	何涛，黄敏超，胡小平，等. 某火箭发动机故障检测及诊断算法设计分析［J］. 南京航空航天大学学报， 2019， 51（）： 50-55.
	HE T， HUANG M C， HU X P， et al. Design analysis of fault detection and diagnosis algorithms for rocket engine［J］. Journal of Nanjing University of Aeronautics & Astronautics， 2019， 51（Sup 1）： 50-55 （in Chinese）.
7	吴建军，朱晓彬，程玉强，等. 液体火箭发动机智能健康监控技术研究进展［J］. 推进技术， 2022， 43（1）： 1-13.
	WU J J， ZHU X B， CHENG Y Q， et al. Research progress of intelligent health monitoring technology for liquid-propellant rocket engines［J］. Journal of Propulsion Technology， 2022， 43（1）： 1-13 （in Chinese）.
8	吴建军，程玉强，崔星. 液体火箭发动机健康监控技术研究现状［J］. 上海航天（中英文）， 2020， 37（1）： 1-10.
	WU J J， CHENG Y Q， CUI X. Research status of the health monitoring technology for liquid rocket engines［J］. Aerospace Shanghai （Chinese & English）， 2020， 37（1）： 1-10 （in Chinese）.
9	赵万里，郭迎清，杨菁，等. 液体火箭发动机故障诊断器设计及其HIL验证［J］. 北京航空航天大学学报， 2019， 45（10）： 1995-2002.
	ZHAO W L， GUO Y Q， YANG J， et al. Design of liquid rocket engine fault diagnosis device and its HIL verification［J］. Journal of Beijing University of Aeronautics and Astronautics， 2019， 45（10）： 1995-2002 （in Chinese）.
10	薛薇，张强，武小平. 基于ARMA模型的液体火箭发动机实时故障诊断方法研究［J］. 计算机测量与控制， 2019， 27（9）： 4-7， 22.
	XUE W， ZHANG Q， WU X P. Based on the ARMA model for the liquid rocket propulsion fault detection［J］. Computer Measurement & Control， 2019， 27（9）： 4-7， 22 （in Chinese）.
11	张晨曦，唐曙，唐珂. 迁移学习下的火箭发动机参数异常检测策略［J］. 计算机应用， 2020， 40（9）： 2774-2780.
	ZHANG C X， TANG S， TANG K. Strategies of parameter fault detection for rocket engines based on transfer learning［J］. Journal of Computer Applications， 2020， 40（9）： 2774-2780 （in Chinese）.
12	殷锴，钟诗胜，那媛，等. 基于BP神经网络的航空发动机故障检测技术研究［J］. 航空发动机， 2017， 43（1）： 53-57.
	YIN K， ZHONG S S， NA Y， et al. Research on aeroengine fault detection technology based on BP neural network［J］. Aeroengine， 2017， 43（1）： 53-57 （in Chinese）.
13	赵松波. 基于改进优化算法的液体火箭发动机故障检测与诊断研究［D］. 天津：天津理工大学， 2019： 7-19.
	ZHAO S B. Research on fault detection and diagnosis of liquid rocket engines based on improved optimization algorithms［D］. Tianjin： Tianjin University of Technology， 2019： 7-19 （in Chinese）.
14	李宁宁，武小平，薛薇，等. 基于遗传算法的大推力氢氧补燃发动机故障检测［J］. 计算机测量与控制， 2022， 30（8）： 14-18， 43.
	LI N N， WU X P， XUE W， et al. Fault diagnosis of high-thrust LOX/LH2 staged combustion cycle engines base on genetic algorithm［J］. Computer Measurement & Control， 2022， 30（8）： 14-18， 43 （in Chinese）.
15	XU L， ZHAO S B， LI N N， et al. Application of QGA-BP for fault detection of liquid rocket engines［J］. IEEE Transactions on Aerospace and Electronic Systems， 2019， 55（5）： 2464-2472.
16	SRINIVAS M， PATNAIK L M. Adaptive probabilities of crossover and mutation in genetic algorithms［J］. IEEE Transactions on Systems， Man， and Cybernetics， 2002， 24（4）： 656-667.
17	YU H H， WANG T. A method for real-time fault detection of liquid rocket engine based on adaptive genetic algorithm optimizing back propagation neural network［J］. Sensors， 2021， 21（15）： 5026.
18	吴陈，王和杰. 基于改进的自适应遗传算法优化BP神经网络［J］. 电子设计工程， 2016， 24（24）： 29-32， 37.
	WU C， WANG H J. BP neural network optimized by improved adaptive genetic algorithm［J］. Electronic Design Engineering， 2016， 24（24）： 29-32， 37 （in Chinese）.
19	HU H， PAN H F， HE Y， et al. Autonomous control technologies of the new generation launch vehicle［J］. Aerospace China， 2021， 22（2）： 25-34.
20	董周杰，郭迎清. 基于综合模糊聚类算法的液体火箭发动机故障诊断［J］. 航空动力学报， 2020， 35（6）： 1326-1334.
	DONG Z J， GUO Y Q. Fault diagnosis of liquid rocket engine based on comprehensive fuzzy clustering algorithm［J］. Journal of Aerospace Power， 2020， 35（6）： 1326-1334 （in Chinese）.
21	DAVIDSON M， STEPHENS J. Advanced health management system for the space shuttle main engine［C］‍∥40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit. Reston： AIAA， 2004.
22	CHA J， KO S， PARK S Y， et al. Fault detection and diagnosis algorithms for transient state of an open-cycle liquid rocket engine using nonlinear Kalman filter methods［J］. Acta Astronautica， 2019， 163： 147-156.
23	宋超，宋娟. 基于遗传算法优化和BP神经网络的短期天然气负荷预测［J］. 工业控制计算机， 2012， 25（10）： 82-84.
	SONG C， SONG J. Parameter optimazation for BP neural network with GA on short-term gas load prediction［J］. Industrial Control Computer， 2012， 25（10）： 82-84 （in Chinese）.
24	JI H Q. Statistics Mahalanobis distance for incipient sensor fault detection and diagnosis［J］. Chemical Engineering Science， 2021， 230： 116233.
25	李艳军. 新一代大推力液体火箭发动机故障检测与诊断关键技术研究［D］. 长沙：国防科技大学， 2014： 14-53.
	LI Y J. Research on key technologies of fault detection and diagnosis for a new generation of large thrust liquid rocket engines［D］. Changsha： National University of Defense Technology， 2014： 14-53 （in Chinese）.
26	黄强. 高压补燃液氧煤油发动机故障检测与诊断技术研究［D］. 长沙：国防科技大学， 2012： 16-29.
	HUANG Q. Research on fault detection and diagnosis technology for high-pressure staged combustion liquid oxygen-kerosene engines［D］. Changsha： National University of Defense Technology， 2012： 16-29 （in Chinese）.
27	张惠军. 液体火箭发动机故障检测与诊断技术综述［J］. 火箭推进， 2004， 30（5）： 40-45.
	ZHANG H J. Study on liquid rocket engine fault detection and diagnostic technology［J］. Journal of Rocket Propulsion， 2004， 30（5）： 40-45 （in Chinese）.
28	李京浩. 基于数据挖掘技术的液体火箭发动机故障检测和诊断研究［D］. 长沙：国防科技大学， 2007： 24-76.
	LI J H. Research on fault detection and diagnosis of liquid rocket engines based on data mining technology［D］. Changsha： National University of Defense Technology， 2007： 24-76 （in Chinese）.
29	刘洪刚. 液体火箭发动机智能故障诊断理论与策略研究［D］. 长沙：国防科技大学， 2002： 12-67.
	LIU H G. Research on intelligent fault diagnosis theory and strategy for liquid rocket engines［D］. Changsha： National University of Defense Technology， 2002： 12-67 （in Chinese）.
30	吴玉洋，李宁宁，薛薇，等. 改进PSO优化LSSVM的液体火箭发动机故障检测［J］. 计算机仿真， 2020， 37（5）： 49-54.
	WU Y Y， LI N N， XUE W， et al. Fault diagnosis of liquid-propellant rocket engines base on improved PSO to optimize LSSVM［J］. Computer Simulation， 2020， 37（5）： 49-54 （in Chinese）.
31	黄敏超，张育林，陈启智. 神经网络在液体火箭发动机故障检测中的应用（Ⅱ）模式识别技术［J］. 推进技术， 1999， 20（2）： 1-4.
	HUANG M C， ZHANG Y L， CHEN Q Z. Neural network approach to fault detection of liquid rocket engine （Ⅱ）pattern recognition technology［J］. Journal of Propulsion Technology， 1999， 20（2）： 1-4 （in Chinese）.

故障检测时间延迟/s	86次有故障试验中出现次数/次	86次有故障试验中占比/%
0.1~0.5	31	36.05
0.5~1	34	39.53
1~2	18	20.93
误检	3	3.49
漏检	0	0

故障检测时间延迟/s	BP神经网络不同时间延迟占比/%	AGABP神经网络不同时间延迟占比/%
0.1~0.5	31.43	40.63
0.5~1.5	48.57	41.90
1.5~2	16.83	13.97
误检	3.17	3.49
漏检	0	0

[1]	Mou CHEN, Zhengguo HUANG, Yaohua SHEN, Fan LIU. Overview of composite anti-disturbance control technology of advanced vehicles [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(6): 531303-531303.
[2]	Zhichun YANG, Te YANG. Physical embedded neural network model and method for dynamic load identification [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(5): 531450-531450.
[3]	Chenhao ZHAO, Dewei WU, Jing HE, Qian WU. A semantic feature matching algorithm for UAV visual pose estimation [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(2): 330406-330406.
[4]	Zibo LIU, Ran ZHANG, Wenchao XUE, Huifeng LI. Active disturbance rejection control for load relief of launch vehicles considering elastic effects [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(1): 330319-330319.
[5]	Fei MA, Qiong ZHANG, Peijun LAI, Yidi YUE. BP neural network⁃based quantitative classification model for safety in experimental flight training [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(5): 529957-529957.
[6]	Yude NI, Miaoyu YAN, Ruihua LIU. Short-term prediction of ionospheric TEC based on DOA-BP neural network [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(4): 328707-328707.
[7]	Jiahao GE, Jinwu XIANG, Daochun LI. Online prediction of non-stationary chaotic time series with noise based on combinational NARX neural network [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(21): 330128-330128.
[8]	Junshen LI, Xiangyan MENG, Nuannuan SHI, Wei LI, Ninghua ZHU, Ming LI. Intelligent processing of optical neural networks: Technological evolution and future prospects [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(20): 630439-630439.
[9]	Yonggang CHEN, Kangni LIU, Shuai WANG. Fault knowledge graph construction for aviation equipment based on BiGRU⁃Attention improvement [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(18): 229916-229916.
[10]	Weiqing HUANG, Ning LI, Kailin LIU, Zhizhong FU, Pengfei JI, Shengliang ZHANG, Liwei DONG, Yantao SUN. Damage state evaluation method of service turbine blades based on MAML-LSTM [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(18): 429547-429547.
[11]	Hui LI, Yinchao CHEN, Shaoshan SUN, Zhaoxin LIANG, Gang MAO, Bin QIAO, Yongbo LI. Fault diagnosis method of rotor system based on federated graph network [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(17): 530611-530611.
[12]	Jiajun LONG, Chenpiao LIU, Fei QIN, Jiale ZHANG, Shengguan XU, Yisheng GAO. Liutex based data-driven turbulence model correction [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(15): 129579-129579.
[13]	Yuanhao HU, Yibo SONG, Jiahui LIU, Xiaoli ZHAO, Wenxiang DENG, Jian HU, Jianyong YAO. Fault diagnosis of electro-hydraulic proportional servo valves based on attention convolutional capsule networks [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(15): 630407-630407.
[14]	Yin DIAO, Zhi ZHANG, Yue PENG, Yang LI, Borong ZHANG, Zhiguo ZHANG. Energy management and trajectory optimization technology for rocket landing [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(15): 229634-229634.
[15]	Yanfang LIU, Jiayu SHE, Qiufan YUAN, Rui ZHOU, Naiming QI. Real⁃time small target detection networks for UAV remote sensing [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(14): 630119-630119.

Fault diagnosis of thrust offset loss of launch vehicle based on AGABP neural network

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 31

Related Articles 15

Recommended Articles

Metrics

Comments