为提高装备故障诊断效率,提出了一种新的粗糙集属性约简方法。首先将属性约简问题转化为集合覆盖问题,在相关矩阵的基础上构建相关系数矩阵;然后基于优质特征集选取原则,引入随机变异机制,设计扰动搜索算法,求出属性约简结果;最后,用UCI数据集和某型复杂装备电源系统进行验证。仿真结果表明,在满足分类精度要求的基础上,有效剔除了冗余属性,缩短了故障诊断时间。

A new attribute reduction method of rough set was proposed to improve the efficiency of fault diagnosis. First, the attribute reduction problem was transformed into the set covering problem and the correlation coefficient matrix was built based on the correlation matrix. Then based on the selection principle of high quality feature set, the random mutation mechanism was introduced, and the disturbance searching algorithm was designed and the result of attribute reduction was obtained. At last, the UCI data set and the power system of one complex equipment were used to validate the algorithm. The simulation results indicated that the redundancy attribute was eliminated effectively and the fault diagnosis time was shortened on the basis of meeting the requirements of the classification accuracy.