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

被引：0
|
作者：
Zhang X. [1 ]
Ren F. [1 ]
Xu P. [1 ]
Li Z. [1 ]
Su C. [1 ]
机构：
[1] Shanghai Aerospace System Engineering Institute, Shanghai
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2023年 / 44卷 / 22期
关键词：
agglomerative hierarchical clustering; liquid rocket engine; measuring parameters selection; model-based method; particle swarm optimization; principal component analysis;
D O I：
10.7527/S1000-6893.2023.28522
中图分类号：
学科分类号：
摘要：
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. © 2023 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.
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