论文部分内容阅读
为了对复杂动态系统部件进行有效的重要度分析,在构建基于事件树-动态故障树(ET-DFT)的概率安全评价模型的基础上,把ET-DFT模型映射为离散时间贝叶斯网络(DTBN),给出各静态和动态逻辑门向DTBN转化的方法以及各逻辑门条件概率表的计算方法。利用DTBN节点的独立性和双向推理功能,给出ET-DFT分层模型FV、RRW、BM和RAW等重要度的计算方法。数控机床液压系统应用实例的分析验证结果表明,基于离散时间贝叶斯网络的复杂机械系统重要度计算方法既能有效得到元件在各时间区间内的重要度,又能准确求出系统故障时各元件在各时间区间的故障概率以及某元件在某时间区间故障时各节点的故障概率。
In order to analyze the importance of complex dynamic system components effectively, the ET-DFT model is mapped to the discrete-time Bayesian network based on the event-tree dynamic fault tree (ET-DFT) DTBN). The methods of transforming each static and dynamic logic gate into DTBN and the method of calculating the conditional probability table of each logic gate are given. Utilizing the independence of DTBN node and bidirectional inference function, the calculation methods of the importance degree of ET-DFT hierarchical model such as FV, RRW, BM and RAW are given. Analysis and verification of numerical control machine hydraulic system application examples show that the method of calculating the importance of complex mechanical systems based on discrete-time Bayesian networks not only can effectively obtain the importance of components in each time interval, but also can accurately find out The probability of failure of components in each time interval and the failure probability of each node in the failure of a component in a certain time interval.