航空学报 > 2010, Vol. 31 Issue (2): 277-284

航空机电系统测试性建模与分析新方法

代京, 张平, 李行善, 于劲松   

  1. 北京航空航天大学 自动化科学与电气工程学院
  • 收稿日期:2008-12-14 修回日期:2009-03-11 出版日期:2010-02-25 发布日期:2010-02-25
  • 通讯作者: 代京

Novel Approach for Aviation Electromechanical System Testability Modeling and Analysis

Dai Jing, Zhang Ping, Li Xingshan, Yu Jinsong   

  1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics
  • Received:2008-12-14 Revised:2009-03-11 Online:2010-02-25 Published:2010-02-25
  • Contact: Dai Jing

摘要: 针对航空机电系统测试性设计(DFT)需求,提出基于面向对象的贝叶斯网络(OOBN)与状态-测试关联灵敏度指标的系统测试性建模与分析的新方法。该建模方法能清晰地刻画系统故障与测试间的关联程度,反映复杂系统的层次结构关系。基于信息论的交叉熵原理提出状态-测试关联灵敏度指标,并给出计算方法。该指标反映复杂机电系统测试中的不确定性影响,克服了基于香农熵的测点评判分析方法的缺点,结合测试性建模获得的模型信息进行推理计算,可用于测试性的定量分析。运用该综合分析方法对飞机燃油系统进行测试性建模与分析,结果表明所提出的方法与指标在航空机电系统DFT中具有实用性。

关键词: 测试性设计, 建模, 面向对象的贝叶斯网络, 交叉熵, 不确定性分析

Abstract: In order to meet the demand of design for testability (DFT) for aviation electromechanical systems, a novel approach for system testability modeling and analysis is proposed which is based on object oriented Bayesian networks(OOBN)and the state-test association sensitivity index. The modeling approach can describe clearly the inter-dependent relationship between system faults and test symptoms, and reflect the hierarchical structure of complex systems. Based on the cross entropy principle in information theory, a novel index named state-test association sensitivity index is proposed together with its calculating methods. The index can reflect the impact of uncertainties in complex electromechanical system tests, and overcome the shortcomings of the test point analysis approach based on Shannon entropy. Combined with the testability model information, it can be used in quantitative testability analysis. The approach is used on an aircraft fuel system testability mo-deling and analysis, and the result shows the practicability of the proposed approach and index in aviation electromechanical system DFT.

Key words: design for testability, modeling, object oriented Bayesian networks, cross entropy, uncertainty analysis

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