从不完备数据中获取诊断规则的粗糙集方法

Extracting Optimal Generalized Decision Rules for Fault Diagnosis from Incomplete Data Based on Rough Set

在故障诊断中,从不完备数据中获取规则要比从完备数据中获取规则困难。利用给出的分辨矩阵基元的定义,提出了一种直接从不完备数据中获取最优广义诊断决策规则的粗糙集方法。该方法以极大相容块为单位构造了不完备故障决策表的分辨矩阵中的列元素,实现了不完备故障诊断决策表中面向对象的约简计算和最优广义故障诊断规则的获取。该方法不需要改变原始不完备故障诊断决策表的规模,且具有更高的约简计算效率。结合电力系统操作点的安全状态诊断实例给出了所提出的方法在工程实践中的应用步骤,并证明了该方法的有效性。

In fault diagnosis, the extraction of rules from incomplete data is usually more difficult than from complete data. By means of the new definition of discern ability matrix primitive, a method for directly extracting optimal generalized decision rules for fault diagnosis from incomplete data based on the rough set is proposed. By using the maximal consistent blocks as column units to construct the discern ability matrices, all object-oriented reductions are found and all optimal generalized decision rules for fault diagnosis from incomplete data are extracted. The method proposed does not require a change in the size of the original incomplete data set, and has higher efficiency of computing reduction. The application of the method is demonstrated with a fault diagnosis example of the operational state of an electric system with incomplete data. The validity of this method is proved.