基于属性差异的产品缺陷数据关联规则模糊分类  

Fuzzy classification of defect data association rules based on attribute differences

作　　者：李怡[1] 孔建益[1] 王兴东[1] 刘军伟[1] Li Yi Kong Jianyi Wang Xingdong Liu Junwei(College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081 ,China)

机构地区：[1]武汉科技大学机械自动化学院,湖北武汉430081

出　　处：《武汉科技大学学报》2017年第1期49-54,共6页Journal of Wuhan University of Science and Technology

基　　金：国家自然科学基金面上项目(51174151);湖北省重大科技创新计划项目(2013AAA011);湖北省自然科学基金资助项目(2013CFA131)

摘　　要：针对工业生产过程中所产生的产品缺陷数据经过数据挖掘后关联规则存在不能有效组织的问题,提出一种基于项目属性差异的产品缺陷数据关联规则模糊分类方法,在建立模糊分类树的基础上,计算出关联规则间距离,并采用自组织神经网络聚类的方法对挖掘结果进行聚类分析。将该方法应用于冷轧带钢表面缺陷数据挖掘后处理,结果表明,该方法不仅能够得出两种不同属性项目间的关联性,还可以求出缺陷关联规则间的距离,距离越近的关联规则被聚为一类,其相似性越大。In light of the fact that the association rules for defect data produced in the industrial process cannot be effectively organized after data mining, this paper proposes a fuzzy method for classification of defect data association rules on the basis of project attribute differences. Based on the fuzzy struc- ture tree, the distance between the association rules is calculated, and the result is analyzed by the method o{ self-organizing neural network clustering. The proposed method is applied to the clustering analysis of data mining on the surface defects of cold rolled strip. The results show that the proposed method can not only obtain the correlation between two different attribute items but also find the dis- tance between the defect association rules. The closer the distance association rules that are grouped into one class, the more similar they are.

关 键 词：冷轧带钢 产品缺陷 属性 关联规则 模糊分类 距离 聚类分析 可视化 

分 类 号：TH164[机械工程—机械制造及自动化]