机载燃油系统在线实时健康管理

冯威; 于劲松; 袁海文; 刘浩

机载燃油系统在线实时健康管理

北京航空航天大学自动化科学与电气工程学院, 北京 100191

基金项目: 航空科学基金资助资助项目（2010ZD11007，20100751010）

详细信息

中图分类号: TP206⁺.3
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- 文章访问数: 1558
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- 被引次数: 0
出版历程
- 收稿日期: 2013-01-21
- 网络出版日期: 2013-12-30

Online real-time health management for aerial fuel delivery system

School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 针对机载燃油系统在线实时健康管理存在的观测信息不确定性、任务完成时限性的问题，研究了国内外最新健康管理算法，提出了机载燃油系统健康模型构建、在线实时推理的方法.该方法基于系统结构模型，采用面向对象方法构建BN（Bayesian Network）健康模型，并利用GVE（Global Variable Elimination）算法离线编译BN健康模型，构造AC（Arithmetic Circuit）健康模型.仿真结果表明：与BN健康模型相比，所设计的AC健康模型在观测信息存在不确定性的情况下，能够高精度在线诊断系统故障，也可以有效满足健康管理严格时限性要求.
- 贝叶斯网络 /
- 健康管理 /
- 燃油系统 /
- 在线实时 /
- 运算电路
Abstract: To address the problems of observational information uncertainties and task deadlines encountered in the online real-time health management(HM) for aerial fuel system(AFS), the latest HM algorithms in China and aboard were researched, and an approach for health modeling and online real-time reasoning of AFS was proposed. The object-oriented method was used to develop BN health model based on the system structure model, and the global variable elimination(GVE) algorithm was applied to the offline compilation of this BN into an arithmetic circuit(AC) health model. The simulation results show that the AC, compared with the Bayesian network, can not only provide precisely online diagnosis of system faults, but also effectively meet the strict time deadlines for HM requirements, under the condition of observational information uncertainties.
- Bayesian networks /
- health management /
- fuel system /
- online real-time /
- arithmetic circuit

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参考文献(12)

[1]	He Fei, Brown M, Yue Hong.Maximin and bayesian robust experimental design for measurement set selection in modelling biochemical regulatory systems[J].International Journal of Robust and Nonlinear Control, 2010, 20(9):1059-1078
[2]	Fu Minyue, Xie Lihua.Quantized feedback control for linear uncertain systems[J].International Journal of Robust and Nonlinear Control, 2010, 20(8):843-857
[3]	Manno G, Chiaccho F.MatCarloRe:an integrated fault tree and monte carlo simulink tool for the reliability assessment of dynamic fault tree[J].Expert Systems with Applications, 2011, 39(12): 10334-10342
[4]	Deb S, Pattipati K R, Raghavan V, et al.Multi-signal flow graphs:a novel approach for system testability analysis and fault diagnosis[J].Aerospace and Electronic Systems Magazine, IEEE, 1995, 10(5):14-25
[5]	Ferreira C C T, Serra G L O.Fuzzy methodology for frequency response estimation of uncertain dynamic systems[J].International Journal of Robust and Nonlinear Control, 2012, 22(9):959-980
[6]	Hagan M T, Demuth H B, Jesús O D.An introduction to the use of neural networks in control systems[J].International Journal of Robust and Nonlinear Control, 2002, 12(11):959-985
[7]	Pearl J.Causality:models, reasoning, and inference[J].ⅡE Transactions, 2002, 34(6):583-589
[8]	Choi A, Zheng Lu, Darwiche A, et al.A tutorial on bayesian networks for system health management[J].Chapman and Hall/CRC Press, 2011, 10(1):1-29
[9]	Chavira M, Darwiche A.Compiling bayesian networks with local structure[C]//Kitano H.International Joint Conferences on Artificial Intelligence-05.Edinburgh, UK:Lawrence Erlbaum Associates Ltd, 2005:1306-1312
[10]	Darwiche A.A compiler for deterministic, decomposable negation normal form[C]//Manuela V.Proceedings of AAAI-02.Edmonton, AB:AAAI Press, 2002:627-634
[11]	Saha B.A model-based reasoning architecture for system-level fault diagnosis[D].Georgia:Georgia Institute of Technology, 2008
[12]	Huang C, Darwiche A.Inference in belief networks:a procedural guide[J].International Journal of Approximate Reasoning, 1996, 15(3):225-263