SFT下的云化概率和关键重要度分布的实现与研究

Realization and research on cloud of probability importance distribution and key importance distribution in SFT

为了完善空间故障树(space fault tree,SFT)理论,特别是离散型空间故障树(discrete space fault tree,DSFT),考虑系统实际运行过程中产生的具有离散性、随机性和模糊性的可靠性数据,提出了云化SFT方法。该方法利用云模型能表示数据的离散性、随机性和模糊性特点,重构SFT的计算基础,即特征函数。进而在SFT计算中保留原始数据特征,使最终结果也能诠释原始数据特征。主要完成了云化SFT理论环节中的一部分,即云化概率重要度分布和关键重要度分布,论证了引入云模型表示系统可靠性数据的必要性和可行性,给出云化概率重要度分布和关键重要度分布的计算推导过程。通过实例对这两个云化概念进行了计算。研究表明云化SFT结果要比原SFT结果更为接近现实,包含了更多数据的原始特征。

In order to improve the adaptability of reliability data of discrete space fault tree （DSFT）, which has the discreteness, randomness and fuzziness in system operation process, this paper put forward the cloud SFT method. This method could represent the data considering discreteness, randomness and fuzzy features based on cloud model. It reconstructed characteristic function as the calculation basis of SFT. The method retained the original data characteristics in the SFT calculation, and could make the final result also interpret the characteristics of original data. It mainly completed a part of the cloud SFT theory, namely cloud probability importance distribution and cloud key importance distribution. It demonstrates the necessity and feasibility on the introduction of cloud model represents the system reliability data. It gave the derivation calculation process of cloud probability importance distribution and key importance distribution. And through the example, it calculated the above two cloud concept. Studies show that cloud SFT results than the original SFT is more close to reality, includes more features of the original data.