• 论文 •

### 面向复杂系统的三维Bayes网络测试性验证模型

1. 1. 海军航空大学, 烟台 264001;
2. 海军研究院, 北京 100161
• 收稿日期:2018-11-19 出版日期:2019-07-20 发布日期:2019-07-25
• 通讯作者: 史贤俊 E-mail:sxjaa@sina.com
• 作者简介:史贤俊 男,博士,教授。主要研究方向:自动测试与故障诊断,测试性设计、验证与评估;王康 男,博士研究生。主要研究方向:测试性设计、验证与评估;肖支才 男,博士,副教授。主要研究方向:自动测试与故障诊断,测试性设计、验证与评估;龙玉峰 男,博士研究生。主要研究方向:测试性设计、验证与评估;陈垚 男,硕士,研究员。主要研究方向:测试与故障诊断。

### Three-dimensional Bayes network testability verification model for complex systems

SHI Xianjun1, WANG Kang1, XIAO Zhicai1, LONG Yufeng1, CHEN Yao2

1. 1. Naval Aviation University, Yantai 264001, China;
2. Naval Research Institute, Beijing 100161, China
• Received:2018-11-19 Online:2019-07-20 Published:2019-07-25

Abstract: In view of the complicated system structure of current weapon equipment, the existing testability verification method based on the testability prior information of the equipment system is difficult to apply, and the testability verification method based on the testability prior information of the subsystem cannot process the prior information effectively, which lead to the low credibility of the testability verification results, so a three-dimensional Bayes network testability verification model for complex systems is proposed. The model can fully utilize the conditional independence contained in the various hierarchical structures of the equipment, effectively reduce the complexity of constructing the Bayesian network model, and at the same time integrate the prior information of each hierarchical unit. Through the given conditional probability learning method and G/M-H algorithm of the three-dimensional Bayes network, the underlying unit data can be integrated through the model to obtain the posterior distribution of the top-level testability indicators, and the top-level posterior distribution is further used to obtain the fault sample size. The results show that the model can fully consider the system structure of the complex system and the prior information of each hierarchical unit, and the posterior distribution of the testability indicators can be used to reduce the fault sample size of testability verification.