基于多信号流图与差分进化算法的测点布局优化

Test point placement optimization based on multi-signal flow graph and differential evolution algorithm

为提高系统可测试性设计过程中的测点布局优化效率,提出了一种基于多信号流图与差分进化算法的测点布局优化方法。该方法首先建立基于多信号流图的系统模型,随后根据系统模型获得测试与故障模式的依赖矩阵,最后根据测点布局对探测率、隔离率和测点数量等方面的灵活需求,通过依赖矩阵和差分进化算法寻找最优测点组合。仿真实验和真实应用案例均证明了该方法的有效性。同时,与基于遗传算法的同类方法的对比实验,还证明了本文方法能更快且更稳定地找到最优测点组合,因此更适用于大型复杂系统的设计。

To improve the test point placement optimization efficiency in system testability design process, a test point placement optimization method based on multi-signal flow graph and differential evolution algorithm is proposed. In this method, an object system model based on multi-signal flow graph is firstly built. Based on this model, a dependency matrix of tests and failure modes is generated. Then, according to the flexible demandsof test point placement forfault detection rate, fault isolation rate and number of test points, the dependency matrix and differential evolution algorithm arecombined to find the optimal test point placement solution. Simulation and practical application cases demonstrate the effectiveness of this method. Moreover, the results of the comparison experiment with the conventional method based on genetic algorithm also demonstrate that the proposed method can obtain the optimum test point combination more stably and faster, which makesit more suitable for the testability design of large scale complex systems.