基于极大相容块的不完备信息处理新方法及其应用  

A new method of incomplete information processing based on maximal consistent block and its application

作　　者：王敬前 张小红[1,2] Wang Jingqian;Zhang Xiaohong(School of Mathematics and Data Science,Shanxi University of Science and Technology,Xifan,710021,China;Shanxi Joint Laboratory of Artificial Intelligence,Shanxi University of Science and Technology,Xian,710021,China)

机构地区：[1]陕西科技大学数学与数据科学学院,西安710021 [2]陕西省人工智能联合实验室,陕西科技大学,西安710021

出　　处：《南京大学学报（自然科学版）》2022年第1期82-93,共12页Journal of Nanjing University（Natural Science）

基　　金：国家自然科学基金(61976130)。

摘　　要：针对不完备信息提出一种新的基于矩阵方法的极大相容块求取算法与属性约简方法,结合智能分类器给出不完备信息条件下的故障诊断方法.首先,通过矩阵方法计算不完备决策表中的极大相容块;然后,利用所求得的极大相容块,提出一种新的属性约简算法,并与其他方法做对比;最后,将所提出的基于极大相容块的属性约简方法与智能分类器(支持向量机.随机森林、决策树等)结合,建立优化的智能故障分类器,将它应用于不完备信息条件下的故障诊断.以汽轮机组的故障诊断为例进行仿真实验,实验结果表明提出的针对不完备信息条件下的故障诊断方法可行、有效.In this paper,a new maximum compatible block algorithm and attribute reduction method based on matrix method are proposed for incomplete information,and the fault diagnosis method under the condition of incomplete information is given by the maximal consistent block and intelligent classifiers.Firstly,the maximal consistent block in an incomplete decision table is calculated by matrices.Then,a new attribute reduction algorithm is proposed based on the maximal consistent block and compared with other methods.Finally,some optimized intelligent fault classifiers are established by the combination between the proposed attribute reduction method and corresponding classifiers,such as support vector machine,random forest and decision tree.It is applied to fault diagnosis under the condition of incomplete information.Moreover,the fault diagnosis of a steam turbine as an example for the simulation.Experimental results shows that the proposed method is feasible and effective.

关 键 词：极大相容块 覆盖粗糙集 矩阵方法 不完备信息 故障诊断 

分 类 号：TP30[自动化与计算机技术—计算机系统结构]