基于贝叶斯网络的测试性分层建模方法  

A Hierarchical Testability Modeling Method Based on Bayesian Network

作　　者：韩露 史贤俊 翟禹尧 HAN Lu;SHI Xianjun;ZHAI Yuyao(Naval Aviation University, Yantai 264000 China)

机构地区：[1]海军航空大学,山东烟台264000

出　　处：《电光与控制》2021年第10期49-54,共6页Electronics Optics & Control

摘　　要：针对目前考虑不确定因素的测试性模型存在建模难度高、结构复杂、层次化建模困难、学习能力弱、不确定信息表达形式差的问题,面向电子设备提出了一种基于贝叶斯网络的测试性分层建模方法。以原有的贝叶斯网络模型与多信号流图模型为研究对象,对二者的参数、结构特点及优势短板进行分析,根据二者的互补性与测试性建模需求对贝叶斯网络模型进行改进。模型以故障、信号关系替代故障、测试关系,以信号节点间的连接表征故障传递,利用电路结构与层次划分确定模型结构,采用贝叶斯方法确立模型参数,运用贝叶斯网络推理能力对测试性指标进行计算。以放大滤波电路为例建立模型,通过模型计算测试性指标,并进行了证据处理测试,通过对比多信号模型可知,模型测试性指标计算准确、证据处理能力强,验证了建模方法的有效性与实用性。At presentthe testability models considering uncertain factors have the problems of high modeling complexitycomplicated structuredifficult hierarchical modelingweak learning abilityand poor expression of uncertain information.Oriented to electronic equipmenta hierarchical testability modeling method based on Bayesian network is proposed.The original Bayesian network model and the multi-signal flow graph model are taken as research objectswhose parametersstructural characteristicsadvantages and shortcomings are analyzed.According to their complementary relationship and testability modeling requirementsthe Bayesian network model is improved.The model replaces the fault and test relationship with the fault and signal relationshipand characterizes fault transmission by the connection between signal nodes.The model's structure is determined by using the hierarchical structure of circuitsand the model's parameters are established by using Bayesian networkwhose inference ability is used to calculate testability indexes.Amplification filter circuits are taken as an exampleand a model is built and used to calculate the testability indexesand evidence processing tests are conducted.Compared with the multi-signal modelsthe model can accurately calculate testability indexes with strong evidence processing abilitywhich verifies the effectiveness and practicality of the modeling method.

关 键 词：测试性 贝叶斯网络 建模 多信号流图 不确定信息 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]