基于离散时间贝叶斯网络的动态故障树分析的改良方法

The computational time and computational accuracy of the traditional dynamic fault tree analysis method based on discrete-time Bayesian network are greatly affected by time segmentation. The calculation error of the traditional method based on the compound trapezoidal integral method is analyzed, and an improved dynamic gate transformation method is proposed to compensate its calculation error. Taking a measurement system as an example, the dynamic measurement tree and the Bayesian network are established to verify the feasibility and efficiency of the improved method. The results show that the improved method can obtain the accurate system failure probability when the time segment is small. The improved method effectively compensates the calculation error of the traditional method, improves the calculation accuracy and computational efficiency of the results, and is suitable for the complex system with all kinds of common distribution.