基于核熵成分分析的热轧带钢自适应聚类分析  

Hot rolled strip state clustering based on kernel entropy component analysis

作　　者：何飞[1] 徐金梧[1] 梁治国[1] 王晓晨[1] 

机构地区：[1]北京科技大学国家板带生产先进装备工程技术研究中心,北京100083

出　　处：《中南大学学报（自然科学版）》2012年第5期1732-1738,共7页Journal of Central South University:Science and Technology

基　　金：国家自然科学基金资助项目(50934007;50905013;51004013);高等学校博士学科点专项科研基金资助项目(20110006110027);冶金装备及其控制教育部重点实验室开放基金资助项目(2009A16);国家"十二五"科技支撑计划项目(2011BAE23B00;2012BAF04B02);中国博士后基金资助项目(20110490294);中央高校基本科研业务费资助项目(FRF-AS-11-011B;FRF-TP-12-167A)

摘　　要：为提高热轧带钢力学性能离线检测的针对性和生产过程控制的实时性,提出利用聚类分析方法实现生产状态的聚类,对错分或离群样本进行力学性能的重点检测。常用的高斯核主成分聚类分析中假设数据服从正态分布,以方差大小提取核主成分,而实际生产数据分布复杂,拟采用核熵主成分分析,并自适应选取核参数和聚类数,实现生产状态的自适应聚类。利用实际生产数据进行方法验证,与核主成分聚类分析相比具有更好的聚类结果,聚类正确率从86.23%提高到96.51%,更加有效地提高了质量检测的针对性。In order to detect mechanical properties of hot rolled steel offline more efficiently and to the point,and improve the control timely,the process data are used for clustering analysis to acquire the state in advance.During the mechanical properties detection,the outliers in the same steel grade are regarded as the focal points.On the assumption that the data obey normal distribution,the variance is used as the information metric to extract features in the common method kernel principal component analysis（KPCA） with Gaussian kernel.The actual production data have complex distribution.Then,kernel entropy component analysis（KECA） was used to extract features and the kernel parameter and cluster number were selected adaptively to do the production state clustering.The real hot strip rolling process data are used for model validation,and as a result,the proposed method has better performance on clustering compared with KPCA to enhance the pertinence of quality detection.The clustering accuracy is improved from 86.23% to 96.51%.

关 键 词：热轧带钢 核熵成分分析 聚类分析 力学性能 

分 类 号：TG333.71[金属学及工艺—金属压力加工] TP274.3[自动化与计算机技术—检测技术与自动化装置]