测试不可靠条件下多故障诊断方法

方甲永; 肖明清; 王学奇; 禹航

测试不可靠条件下多故障诊断方法

空军工程大学自动测试系统实验室, 西安 710038

基金项目: 国防十一五重点预研资助项目(51317030103)

详细信息

作者简介:
方甲永(1983－),男,河南濮阳人,博士生,fjylike@sohu.com.

中图分类号: TP 277
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- 被引次数: 0
出版历程
- 收稿日期: 2010-01-22
- 网络出版日期: 2011-04-30

Multiple fault diagnosis method with unreliable test

ATS Lab, Air Force Engineering University, Xi’an 710038, China

摘要

摘要: 针对部队复杂系统故障诊断中存在的诊断精度低,虚警率高等问题,提出一种测试不可靠条件下多故障诊断方法.为解决系统诊断贝叶斯网络结构和概率映射表建立困难的问题,通过建立系统的多信号流图模型,从而获得系统诊断贝叶斯网络.将测试不可靠度引入概率映射表,增加了算法工程应用中的鲁棒性.利用后验概率诊断推理将问题归结为不等式约束极值问题,采用0-1规划隐数法对不等式极值问题求解,从而获得最优解.以某型导弹制导系统电子部件为例,验证了该方法对复杂系统多故障诊断的有效性.
- 多信号流图模型 /
- 诊断贝叶斯网络 /
- 贝叶斯后验概率 /
- 0-1规划隐数法
Abstract: Aiming at low diagnosis precision and high false alarm of complex systems diagnosis, a multiple fault diagnosis method was proposed. In order to solve the difficulties of building Bayesian networks’ structure and conditional probabilities table, the system-s multi-signal flow graphs model was built. By introducing unreliability probability into conditional probabilities table, the proposed method-s robustness was enhanced. The problem was concluded as the extreme value problem with inequality constraints by using Bayes maximal posterior probability. The optimized result was obtained by the 0-1 programming implicit enumeration. The electronic equipment of guidance system was explored to illustrate the effectiveness of the proposed method.
- multi-signal flow graph model /
- diagnostic Bayesian networks /
- Bayes posterior probability /
- 0-1 programming implicit enumeration

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参考文献(1)

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