基于MSFG-BN的层次化测试性模型

Hierarchical testability model based on MSFG-BN

相关性测试性模型如多信号流图(MSFG)处理、学习不确定信息存在困难,贝叶斯网络(BN)模型建模难度高。针对以上模型存在的短板,提出MSFG-BN测试性模型及其建模方法,将贝叶斯网络引入多信号流图模型,提高对不确定信息的处理能力;将信号概念代入贝叶斯网络,减小贝叶斯网络结构复杂度,降低了建模难度。最后通过实例验证,该模型结构直观,易于研究人员理解;故障诊断速度快,证据处理能力强。证明了建模方法具备一定的可用性。

Correlation test models such as multiple signal flow graphs(MSFG) have difficulty in dealing with uncertain information. Bayesian network(BN) models are difficult to model. Aiming at the above problems, the MSFG-BN testability model and its modeling method are proposed, and the bayesian network is introduced into the multi-signal flow diagram model to improve the processing capacity of uncertain information. The signal concept is substituted into bayesian network to reduce the complexity of model structure and reduce the difficulty of modeling. The structure of the model is intuitive and easy for researchers to understand. The fault diagnosis speed is fast, the evidence processing ability is strong. It also proves that the modeling method is available.