A general enhancement method for test strategy generation for the sequential fault diagnosis of complex systems
提出一种基于支持向量机、ECA*和蒙特卡洛的通用增强方法，可应用于现有测试序列生成算法，通过动态调整依赖矩阵规模来降低计算时间和测试成本。
In order to improve the reliability, operational readiness and system safety of equipment, testability should be seriously considered in the design stage. As an important part of design for testability, test sequence generation is a binary identification problem because a minimal expected cost testing procedure must be developed in order to determine the amount of possible failure sources, if any, are present. Many algorithms have been proposed, but the generation time is long or the test cost is high when dealing with a large-scale dependency matrix. To address this issue, we propose a general enhancement method based on the SVM, the ECA* and the Monte Carlo. It can be applied to any existing algorithm and can effectively improve the performance. The available tests are classed based on the SVM according to the information of nodes, the ECA* is used to cluster states, and the morphological function of the test sequence is obtained through the Monte Carlo simulation. All this information is fused to dynamically adjust the scale of the dependency matrix and selected to modify the parameters. Experiments show that the existing algorithms have shorter calculation time and lower costs because the information is considered more comprehensively after enhancement.