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为了充分利用已有的长期监测得到的元件故障记录数据,根据其数据稀疏且离散的特点,在作者提出的连续型空间故障树(CSFT)基础上,提出适合于处理这些数据的离散型空间故障树(DSFT).首先对元件故障记录数据分别按照元件的工作时间t和工作温度c进行统计,然后分别在t和c方向投影且归一化数据,最后对故障概率分布点进行拟合得到特征函数,进而得到元件故障概率空间分布.研究表明:DSFT是由表及里地研究元件的故障概率空间分布.将CSFT与DSFT结合分析得到,除t和c外仍有其它环境因素影响着元件故障概率空间分布,并得到了影响范围和特征.
In order to make full use of the existing long-term monitoring of component fault record data, based on the sparse and discrete data, based on the continuous-type spatial fault tree (CSFT) proposed by the author, a discrete spatial fault suitable for processing these data is proposed Tree (DSFT) .Firstly, the component failure record data are respectively calculated according to the component working time t and the working temperature c, and then the data are respectively projected and normalized in the t and c directions. Finally, the fault probability distribution point is obtained by fitting Function, and then get the probability distribution of component failure.The results show that: DSFT is the probability distribution of the failure probability of the components studied in the table and in the field.Combined with the CSFT and DSFT analysis, there are other environmental factors besides t and c that affect the component failure Probability distribution of space, and have been affected by the scope and characteristics.