Test optimization selection method based on NSGA-3 and improved Bayesian network model

Lu Han,Xianjun Shi,Yuyao Zhai

doi:10.1051/jnwpu/20213920414

Abstract

Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations. To solve these problems, a test optimization selection method based on NSGA-3 algorithm and Bayesian network model is proposed. Firstly, the paper describes the improved Bayesian network model, expounds the method of model establishment, and introduces the model's learning ability and processing ability on uncertain information. According to the constraints and objective functions established by the design requirements, NSGA-3 is used to calculate the test optimization selection scheme based on the improved Bayesian network model. Taking a certain component of the missile airborne radar as an example, the fault detection rate and isolation rate are selected as constraints, and the false alarm rate, misdiagnosis rate, test cost, and test quantity are the optimization goals. The method of this paper is used for test optimization selection. It has been verified that this method can effectively solve the problem of multi-objective test selection, and has guiding significance for testability design.

Highlights

图,可以表示变量间的相互关系与联合概率分布,可以据此特性建立贝叶斯网络测试性模型来描述故障变量与测试变量间的关系,模型示意图如图 1 所示, 模型主要由以下元素构成: 故障有限集: F = { f1,f2,...,fm} ; 可用测试的有限集:T = {t1,t2,...,tn}; 故障 - 测试相关关系 P:条件概率表(CPT); 有向无环图:GDA = { F,T,P,E} ,其中 E 为连接线,表示变量间具有相关关系; 表 2 t1 节点的条件概率表
Test optimization selection method based on NSGA⁃3 and improved Bayesian network model
Most of the solutions to existing test selection problems are based on single⁃objective optimization algo⁃ rithms and multi⁃signal models, which maybe lead to some problems such as rough index calculation and large solu⁃ tion set limitations

Summary

Introduction

图,可以表示变量间的相互关系与联合概率分布,可以据此特性建立贝叶斯网络测试性模型来描述故障变量与测试变量间的关系,模型示意图如图 1 所示, 模型主要由以下元素构成: 故障有限集: F = { f1,f2,...,fm} ; 可用测试的有限集:T = {t1,t2,...,tn}; 故障 - 测试相关关系 P:条件概率表(CPT); 有向无环图:GDA = { F,T,P,E} ,其中 E 为连接线,表示变量间具有相关关系; 表 2 t1 节点的条件概率表中 k 表示信号种数,i,j 表示某信号的节点次序; 故障 - 测试相关关系 P:条件概率表(CPT); 有向无环图:GDA = { F,S,P,E} ,其中 E 为连接线。 Dir( θij | αij1 + Nij1 ,αij2 + Nij2 ,...αijri + Nijri )(3) 参数向量 θs 计算完毕,利用 θs 对案例进行预策略,使得解在目标空间内更加均匀,其概念及计算方法如下: 利用边界交叉构造权重的方法在超平面上均匀地产生 C 个点,这些点就是参考点。设置方法如图 6 所示。 NSGA⁃3 算法通过个体关联参考点的方法来衡量目标空间的均匀程度。

Results

Conclusion