期刊文献+

基于粗糙集的故障诊断特征提取 被引量:20

Fault diagnosis feature subset selection using rough set
在线阅读 下载PDF
导出
摘要 故障的特征提取对于进行准确可靠的诊断非常重要。而实际的故障诊断数据样本的分类边界常常是不确定的,并且故障与征兆之间的关系往往也是不确定的。粗糙集理论是处理模糊和不确定性问题的新的数学工具。论文将粗糙集理论引入到故障诊断特征提取,提出了一种基于粗糙集的故障诊断特征提取方法。并通过两个故障诊断实例对该方法进行了验证。结果表明:在有效地保持故障诊断分类结果的情况下,该方法可以提取出最能反映故障的特征,从而为粗糙集在故障诊断中的深入应用打下了基础。 Feature subset selection is of prime important for effective fault diagnosis.But the classification boundary of real fault diagnosis data sets is often ambiguous,and the relationships between faults and symptoms are always uncertain.Rough set theory is a novel mathematical tool dealing vagueness and uncertainty.This paper introduces rough set theory and proposes a method for fault diagnosis feature subset selection.By two fault diagnosis examples,this paper validates the method.The results show that this method can efficiently extract the main fault features while the fault classification result is invariable.The research in this paper supplies a basis for further study of applying rough set theory in fault diagnosis.
出处 《计算机工程与应用》 CSCD 北大核心 2007年第1期221-224,共4页 Computer Engineering and Applications
关键词 粗糙集理论 特征提取 故障诊断 rough set theory feature selection fault diagnosis
  • 相关文献

参考文献11

  • 1徐章遂 房立清 王希武 等.故障信息诊断原理及应用[M].北京:国防工业出版社,2001..
  • 2Pawlak Z.Rough set[J].International Journal of Computer and Information Science, 11:341-356.
  • 3Pawalk Z.Rough sets [M]//Theoretical Aspects of Reasoning about Data, Boston:Kluwer Academic Publishers, 1991.
  • 4Pawlak Z,Busse J G,Slowinski R,et al.Rough sets[J].Communications of the ACM, 1995,38 ( 1 1 ) : 89-95.
  • 5曾黄麟.粗集理论及其应用[M].重庆:重庆大学出版社,1998..
  • 6Robert.Analying discretization of continuous attributes given a monotonic discrimination function[J].Intelligent Data Analysis, 1997,1:157-179.
  • 7Hung Son Nguyen.Discretization problem for rough sets methods[C]//Proc of the First Int Conf on Rough Sets and Current Trends in Computing.Spring Verlag, 1998:545-552.
  • 8侯利娟,王国胤,聂能,吴渝.粗糙集理论中的离散化问题[J].计算机科学,2000,27(12):89-94. 被引量:104
  • 9代建华,李元香,刘群.粗糙集理论中基于遗传算法的离散化方法[J].计算机工程与应用,2003,39(8):13-14. 被引量:12
  • 10飞思科技产品研发中心.MatLab6.5辅助神经网络分析与设计[M].电子工业出版社,2004..

二级参考文献16

  • 1曾黄麟.粗集理论及其应用-关于数据推理的新方法 (修订版)[M].重庆:重庆大学出版社,1998.83-87.
  • 2[1]Pawlak Z,Grzymala-Bausse J,Slowinski R et al. Rough sets[J].Communications of the ACM, 1995 ;38( 11 ) :89~95
  • 3[2]Pawlak Z,Skowron A.Rough Sets Rudiments[M].Bulletin of IRSS,1999: 67~70
  • 4[3]Ziarko W.Introduction to the special issue on rough sets and knowledge discovery[J].International Journal of Computational Intelligence,1995; 11 (2) :223~226
  • 5[4]Nguyen H S,Skowron A.Quantization of real value attributes[C].In:Proceeding s of Second Joint Annual Conf on Information Science,Wrightsville Beach,North Carolina,1995:34~37
  • 6[5]Nguyen H S.Discretization of Real Value Attributes:Boolean reasoning Approach[D].Ph D Dissertation. Warsaw University,Warsaw,Poland,1997
  • 7曾黄麟,粗集理论及其应用—关于数据推理的新方法.修订版,1998年,83页
  • 8Pawlak Z. Rough sets. International Journal of Information and Computer Science, 1982, (11): 341~356
  • 9刘清.Rough集及Rough推理.北京:科学出版社,2001.11-39
  • 10王国胤.Rough集理论与知识获取.西安:西安交通大学出版社,2001.92-116

共引文献249

同被引文献167

引证文献20

二级引证文献79

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