基于相关性矩阵合并算法的系统级测试性建模方法研究
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Research on System-level Testability Modeling Method Based on Correlation Matrix Merging Algorithm
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    摘要:

    针对大型复杂系统直接构建测试性模型难度较大的问题,提出一种相关性矩阵合并算法。分析大型复杂 系统的层次模块划分原则,对层次模块化测试性模型的建模要素进行了简要阐述。合并算法通过对系统进行层次模 块划分,然后对低层次的单元模块分别进行测试性建模,再将低层次单元模块的故障-测试相关性矩阵逐步合并,进 而生成整个系统的故障-测试相关性矩阵,并以某型导弹的导航系统为例进行实例验证,简化系统级测试性模型构建 的难度。

    Abstract:

    Aiming at the difficulty of building testability model directly for large complex system, a correlation matrix merging algorithm is proposed. Firstly, the principle of hierarchical module division of large complex system is analyzed, and the modeling elements of hierarchical modular testability model are briefly described. The merging algorithm divides the hierarchical modules of the system, and then builds testability models for the low-level unit modules. The fault-test correlation matrix of the low-level unit module is gradually merged to generate the fault test correlation matrix of the whole system. Finally, the navigation system of a certain missile is taken as an example to verify, which simplifies the difficulty of system level testability model construction.

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韩 露.基于相关性矩阵合并算法的系统级测试性建模方法研究[J].,2021,40(8).

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  • 收稿日期:2021-04-30
  • 最后修改日期:2021-05-28
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  • 在线发布日期: 2021-08-17
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