基于故障分辨能力的火箭发动机测量参数选择方法

doi:10.7527/S1000-6893.2023.28522

Abstract

Abstract:

To address the problem of measuring parameters selection of liquid rocket engines， we propose a model-based method to improve fault recognition and system reliability. The nonlinear and static mathematical model of the engine system is built， the fault feature list metrics of fault recognition， robustness and system reliability of the engine measurement feature subset are then established respectively， and the optimization design is proposed based on the improved multi-objective binary particle swarm optimization （MOBPSO）. With the optimized measuring parameters， the number of distinguishable faults has increased from 9 to 13， the robustness is equivalent to the original layout， and the risk criterion has increased slightly. The important role of the subsystem mixture ratio in fault recognition is further explored and its mechanism analyzed. The method proposed in this paper has a good application value for the selection of measurement characteristics of other complex， closed-loop dynamic systems.

Key words: liquid rocket engine, model-based method, measuring parameters selection, particle swarm optimization, agglomerative hierarchical clustering, principal component analysis

CLC Number:

V434+.11

Xiaopu ZHANG, Feng REN, Pengli XU, Zhimin LI, Caihong SU. Selection method of measuring parameters for rocket engine based on fault recognition[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(22): 128522-128522.

Figures/Tables 15

Table 1

Mathematical definition of faults

故障类型	故障模拟方式	故障的数学定义	故障系数取值范围/%
泄漏故障	管路、阀门泄漏点下游流量q'为上游流量q的ζ_x倍（ζ_x<1）	$q' = ζ x q$	0~30
堵塞故障	节流元件堵塞点流阻系数k'为设计值k的ζ_d倍（ζ_d>1）	$k' = ζ d k$	100~3 000
转子效率下降（结构破坏、碰磨）	涡轮、泵转子效率η'为设计值η的ζ_e倍（ζ_e<1）	$η' = ζ e η$	70~100
入口压力异常	入口压力p $o'$ 为设计值p_o的ζ_o倍（ζ_o<1）	$p o' = ζ o p o$	90~120

Table 1

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References 24

1	MAUL W A， KOPASAKIS G， SANTI L M， et al. Sensor selection and optimization for health assessment of aerospace systems［J］. Journal of Aerospace Computing， Information， and Communication， 2008， 5（1）： 16-34.
2	郑帅，王子涵，赵浩然，等. 基于差分进化算法的飞机油量传感器布局优化方法［J］. 航空学报， 2022， 43（8）： 125809.
	ZHENG S， WANG Z H， ZHAO H R， et al. Layout optimization method of aircraft fuel gauging sensor based on differential evolution algorithm［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（8）： 125809 （in Chinese）.
3	JUNG K， YANG J， LEE S， et al. Design optimization of fuel sensor location in aircraft conformal fuel tank［J］. Journal of the Korean Society for Aeronautical & Space Sciences， 2018， 46（4）： 332-337.
4	PEREIRA J L J， FRANCISCO M B， DE OLIVEIRA L A， et al. Multi-objective sensor placement optimization of helicopter rotor blade based on Feature Selection［J］. Mechanical Systems and Signal Processing， 2022， 180： 109466.
5	OMATA N， SATOH D， TSUTSUMI S， et al. Model-based supervised sensor placement optimization to detect propellant leak in a liquid rocket engine［J］. Acta Astronautica， 2022， 195： 234-242.
6	徐敏强，宋其江，王日新. 基于可观测性和可靠性的传感器分布优化设计［J］. 宇航学报， 2010， 31（11）： 2618-2622.
	XU M Q， SONG Q J， WANG R X. Optimization design of sensors location based on fault observability and reliability［J］. Journal of Astronautics， 2010， 31（11）： 2618-2622 （in Chinese）.
7	杨冬健，靳小波，杨占才，等. 基于MINLP模型的航空液压系统传感器布局优化［J］. 测控技术， 2020， 39（5）： 49-53.
	YANG D J， JIN X B， YANG Z C， et al. Optimization of sensor layout in aero-hydraulic system based on MINIP model［J］. Measurement & Control Technology， 2020， 39（5）： 49-53 （in Chinese）.
8	张笑华，吴圣斌，方圣恩，等. 采用Pareto人工鱼群算法的结构健康监测传感器位置多目标优化［J］. 振动工程学报， 2022， 35（2）： 351-358.
	ZHANG X H， WU S B， FANG S E， et al. Multi-objective sensor optimal placement for structural health monitoring based on Pareto artificial fish swarm algorithm［J］. Journal of Vibration Engineering， 2022， 35（2）： 351-358 （in Chinese）.
9	LI B B， LI D S， ZHAO X F， et al. Optimal sensor placement in health monitoring of suspension bridge［J］. Science China Technological Sciences， 2012， 55（7）： 2039-2047.
10	LIU K， YAN R J， GUEDES SOARES C. Optimal sensor placement and assessment for modal identification［J］. Ocean Engineering， 2018， 165： 209-220.
11	KONG X D， CAI B P， LIU Y H， et al. Optimal sensor placement methodology of hydraulic control system for fault diagnosis［J］. Mechanical Systems and Signal Processing， 2022， 174： 109069.
12	LI B B， DER KIUREGHIAN A. Robust optimal sensor placement for operational modal analysis based on maximum expected utility［J］. Mechanical Systems and Signal Processing， 2016， 75： 155-175.
13	LI B， ZHAO Y P， WU H， et al. Optimal sensor placement using data-driven sparse learning method with application to pattern classification of hypersonic inlet［J］. Mechanical Systems and Signal Processing， 2021， 147： 107110.
14	BŁACHOWSKI B， ŚWIERCZ A， OSTROWSKI M， et al. Convex relaxation for efficient sensor layout optimization in large-scale structures subjected to moving loads［J］. Computer-Aided Civil and Infrastructure Engineering， 2020， 35（10）： 1085-1100.
15	TARAVATROOY N， NIKOO M R， HOBBI S， et al. A novel hybrid entropy-clustering approach for optimal placement of pressure sensors for leakage detection in water distribution systems under uncertainty［J］. Urban Water Journal， 2020， 17（3）： 185-198.
16	MORLIER J， BASILE A， CHIPLUNKAR A， et al. An EGO-like optimization framework for sensor placement optimization in modal analysis［J］. Smart Materials and Structures， 2018， 27（7）： 075004.
17	沈赤兵. 液体火箭发动机非线性静特性与响应特征［M］. 北京：科学出版社， 2019.
	SHEN C B. Nonlinear static characteristics and response characteristics of liquid rocket engines［M］. Beijing： Science Press， 2019.
18	GENTLE J E， KAUFMAN L， ROUSSEUW P J. Finding groups in data： An introduction to cluster analysis［J］. Biometrics， 1991， 47（2）： 788.
19	JAIN A K， MURTY M N， FLYNN P J. Data clustering： a review［J］. ACM Computing Surveys， 31（3）： 264-323.
20	DAVIES D L， BOULDIN D W. A cluster separation measure［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 1979（2）： 224-227.
21	DUNN J C. Well-separated clusters and optimal fuzzy partitions［J］. Journal of Cybernetics， 1974， 4（1）： 95-104.
22	PEDRASA M A A， SPOONER T D， MACGILL I F. Scheduling of demand side resources using binary particle swarm optimization［J］. IEEE Transactions on Power Systems， 2009， 24（3）： 1173-1181.
23	黄洋，鲁海燕，许凯波，等. 基于S型函数的自适应粒子群优化算法［J］. 计算机科学， 2019， 46（1）： 245-250.
	HUANG Y， LU H Y， XU K B， et al. S-shaped function based adaptive particle swarm optimization algorithm［J］. Computer Science， 2019， 46（1）： 245-250 （in Chinese）.
24	陈贵敏，贾建援，韩琪. 粒子群优化算法的惯性权值递减策略研究［J］. 西安交通大学学报， 2006， 40（1）： 53-56， 61.
	CHEN G M， JIA J Y， HAN Q. Study on the strategy of decreasing inertia weight in particle swarm optimization algorithm［J］. Journal of Xi’an Jiaotong University， 2006， 40（1）： 53-56， 61 （in Chinese）.

故障发生概率评级	不同严酷度评级下的风险指数
故障发生概率评级	Ⅰ 灾难的	Ⅱ 致命的	Ⅲ 严重的	Ⅳ 轻度的
A级（P>10^-1）	1	3	7	13
B级（10^-2<P≤10^-1）	2	5	9	16
C级（10^-4<P≤10^-2）	4	6	11	18
D级（10^-6<P≤10^-4）	8	10	14	19
E级（P≤10^-6）	12	15	17	20

测量方式	故障模式	局部影响	最终影响	严酷度	可能性	风险指数
氧化剂喷注前压力传感器	氧化剂外漏	推力室性能降低	入轨精度降低	Ⅲ	E	17
转速线圈	元器件失效	转速线圈测量功能失效	对飞行无影响	Ⅳ	D	20

序号	权重取值	I₁	I₂
1	ω=0.9	4.53	50.15
2	ω=0.4	4.60	47.35
3	自适应权重	4.47	44.05

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Selection method of measuring parameters for rocket engine based on fault recognition

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