基于改进的粗糙集和因果图的故障诊断  

Fault Biagnosis Based on Improved Rough Set and Causality Diagram

作　　者：文鸿瑛 WEN Hong-ying(College of Mathematics Science,Chongqing Normal University,Chongqing 401331,China)

机构地区：[1]重庆师范大学数学科学学院,重庆401331

出　　处：《唐山师范学院学报》2022年第6期8-12,共5页Journal of Tangshan Normal University

基　　金：重庆师范大学CS研究生科研创新项目智慧教育专项(YZH21007)。

摘　　要：在粗糙集和因果图属性约简的基础上,先通过构建二进制可分辨矩阵,并精简可分辨矩阵元素,以此约简得出最小属性约简集,再依据最小属性约简集对原始因果图进行约简,最后进行故障诊断。在属性约简时,符号逻辑运算被转变为位逻辑运算,减少了模型的复杂程度,从而提高了推理速度。对某电网的实例,运用该方法,可以减少存储空间,减少复杂性,提升诊断效果。On the basis of attribute reduction of rough set and causality diagram,the minimum attribute reduction set was obtained by building binary distinguishable matrix and simplifying the distinguishable matrix elements,then the original causality diagram was simplified according to the minimum attribute reduction set.-Finally,the fault was diagnosed.In attribute reduction,symbolic logic was transformed into bitwise logic,which reduced the complexity of the model and improved the speed of reasoning.For an example of a power network,this method can reduce the storage space,reduce the complexity and improve the diagnosis effect.

关 键 词：粗糙集 可分辨矩阵 因果图 故障诊断 

分 类 号：O231.5[理学—运筹学与控制论]