基于云模型和SFT的可靠性数据不确定性评价

为了分析和评价系统可靠性数据中的不确定性,即随机性、离散性和模糊性,将云模型与空间故障树(Space Fault Tree,SFT)理论相结合,使用云化SFT作为基础对数据不确定性进行评价。首先使用云模型能表示数据不确定性的特点,将SFT的相关概念进行云化。将其中云化系统故障概率分布对在某因素影响下,可靠性数据生成的云模型特征参数Ex、En和He进行求导。根据在系统工作环境范围内的求导结果,并结合提出的模糊性 、离散性 和随机性 计算数据的不确定性。使用该方法对经典实例进行了分析,得到了一些定性和定量结果。但Q和 两个参数应该具体问题具体分析。

Uncertainty evaluation of the reliability based on cloud model and the SFT

In order to analyze and evaluate the uncertainty in system reliability data, namely the randomness and discreteness and fuzziness, the combination of the cloud model with Space Fault Tree (SFT) theory, the cloud SFT is used as a basis to evaluate data uncertainty. First, the relevant concepts of the SFT is clouded, because the characteristics of cloud model can represent data uncertainty. Cloud system failure probability distribution is derivate with cloud model characteristic parameters (Ex, En and He) generated by reliability data, under the influence of some factors. According to the derivative result in the system working environment, and combining the vagueness, discreteness and randomness, of the data of uncertainty are computed. The classical examples are analyzed by the method, and some qualitative and quantitative results are obtained. But two parameters of Q and should be specific analysis.