期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

数据挖掘在轮胎均匀性试验数据上的应用 被引量：2

Application of Data Mining in Tire Uniformity
下载PDF
导出
摘要 制造执行系统(MES)可以提升轮胎制造企业的运营水平,同时也集成了海量的制造环节数据,能够应用数据挖掘技术进行充分利用和挖掘,使信息更有价值。基于MES数据仓库中提取的轮胎质检工段的均匀性检测数据,采用特征选择的方法分析影响各规格轮胎均匀性的质量因素及权重,比较了基于信息论、统计、相似度的几种算法的应用效果。对于均匀性数据的冗余属性问题,利用LFS+CFS进行分析,发现降维后的属性在预测均匀性等级、归档压缩和质量管理方面展现出了价值。 Abstract： MES system could help the tire manufacturer improve their operations, integrate the manufacturing domain data, and could be benefited by data mining technology. Based on the tire uniformity test data extracted from the MES data warehouse, the influence factors on the tire uniformity attribute were analyzed by feature selection method. The application effects of several kinds of algorithm according to information theory, statistics, similarity were compared. The solution of LFS ＋ CFS was applied to eliminate the uniformity attribute redundancy feature. The attributes value went up by dimensionality reduction in the fields of uniformity forcast, archive compression and quality control.
作者 张健斌 朱兰娟
机构地区 上海交通大学自动化系
出处 《世界橡胶工业》 2016年第7期45-51,共7页 World Rubber Industry
关键词 轮胎均匀性 制造执行系统(MES) 特征选择 数据挖掘 Python程序设计 Tire Uniformity MES Feature Selection Data Mining Python Language
分类号 TP274.2 [自动化与计算机技术—检测技术与自动化装置]
  • 相关文献

参考文献17

  • 1Bharati M. Data Mining Techniques and Applications [J]. Indian Journal of Computer Science and Engineering, 2010, 1(4): 301-305.
  • 2Choudhary A K, Harding J A, Tiwari M K. Data Mining in Manullacturing: A Review Based on the Kind of Knowledge[J]. J. lntell. Manuf~ 2009(20):501-521.
  • 3Rockwell Automation. The Connected Industrial Enterprise White Paper[R/OL].http://literature.rockwellautonlation com/idc/groups/literature/documents/wp/cie-wpOOl_-en-p.pdf.
  • 4Joaquin B, Ordieres M. Data Mining in Industrial Process[J]. Engineering Data Mining and Numerical Simulation, 2005:57-66.
  • 5Rokachl L, Maimon O. Data Mining for Improving the Quality of Manufacturing a Feature Set Deomposition Approach[J]. Journal of Intelligent Manufacturing, 2006, 17(3): 285-299.
  • 6Rudolf K,Matthias S. Christian M. Data Mining Applications in the Automotive Industry[C].Singapore:4th International Workshop on Reliable Engineering Computing, 2010.
  • 7Gunturkun F. A Comprehensive Review of Data Mining Application in Quality Improvement and a Case Study [D] Ankara: The Graduate School of Natural and Applied Sciences of Middle East Technical University,2007.
  • 8WittenLH,FrankEF,HallMA.数据挖掘:使用机器学习工具与技术[M].北京:机械工业出版社.2014:4.
  • 9ANSI ISA 95.01-2000. Enterprise - Control System Integration[S].
  • 10lsabelle G, Andre E. An Introduction to Variable and Feature Selection[J]. Journal of Machine Learning Research 2003(3):1157-1182.

二级参考文献52

  • 1Li G-Z, Yang J Y. Feature selection for ensemble learning and its application[M]. Machine Learning in Bioinformatics, 2008: 135-155.
  • 2Sheinvald J, Byron Dom, Wayne Niblack. A modelling approach to feature selection[J]. Proc of 10th Int Conf on Pattern Recognition, 1990, 6(1): 535-539.
  • 3Cardie C. Using decision trees to improve case-based learning[C]. Proc of 10th Int Conf on Machine Learning. Amherst, 1993: 25-32.
  • 4Modrzejewski M. Feature selection using rough sets theory[C]. Proc of the European Conf on Machine ,Learning. 1993: 213-226.
  • 5Ding C, Peng H. Minimum redundancy feature selection from microarray gene expression data[J]. J of Bioinformatics and Computational Biology, 2005, 3(2): 185-205.
  • 6Francois Fleuret. Fast binary feature selection with conditional mutual information[J]. J of Machine Learning Research, 2004, 5(10): 1531-1555.
  • 7Kwak N, Choi C-H. Input feature selection by mutual information based on Parzen window[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(12): 1667-1671.
  • 8Novovicova J, Petr S, Michal H, et al. Conditional mutual information based feature selection for classification task[C]. Proc of the 12th Iberoamericann Congress on Pattern Recognition. Valparaiso, 2007: 417-426.
  • 9Qu G, Hariri S, Yousif M. A new dependency and correlation analysis for features[J]. IEEE Trans on Knowledge and Data Engineering, 2005, 17(9): 1199- 1207.
  • 10Forman G. An extensive empirical study of feature selection metrics for text classification[J]. J of Machine Learning Research, 2003, 3(11): 1289-1305.

共引文献206

同被引文献11

引证文献2

二级引证文献21

世界橡胶工业
2016年 第7期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部