基于GSPN与BN的测试性建模方法

Testability model method based on GSPN and BN

针对目前主流建模方法是基于测试结果可靠的假设条件前提,难以得到故障和测试之间准确的信息描述的问题,提出基于概率广义随机Petri网的测试性建模新方法。首先对主流建模方式和广义随机Petri网建模方式进行对比,阐明主流建模方式存在的问题,以及选择广义随机Petri网建模的原因;然后对Petri网原理进行分析,并将概率理论引入广义随机Petri网模型中,采用贝叶斯网络获取模型节点的条件概率,也是首次将贝叶斯网络和广义随机Petri网结合;最后对某导弹发动机系统进行建模,得到故障和测试的概率相关性矩阵,并对测试性指标进行求解。通过对比原有模型的相关性矩阵和本文所得到的概率相关性矩阵,验证了所提方法的可行性和准确性。

Aiming at the premise that the current mainstream modeling method is based on the reliable test results, it is difficult to obtain an accurate information description problem between fault and test. A new test-based modeling method based on probabilistic generalized stochastic Petri net is proposed. This paper first compares the mainstream modeling method with the Petri net modeling method, clarifies the problems existing in the mainstream modeling method, and the reasons for selecting the generalized stochastic Petri net model;then introduces the probability theory into the generalized stochastic Petri net model, using the conditional probability of the model nodes obtained by the Bayesian network, and it is also the first combination of Bayesian network and generalized stochastic Petri net. Finally, a missile engine system is modeled to obtain the probability correlation matrix of faults and tests, and the test indicators are tested. The feasibility and accuracy of the proposed method are verified by comparing the original correlation matrix with the probability correlation matrix obtained in this paper.