基于奇异点检测和模糊粗糙集相结合的故障特征降维方法及应用研究  

Application Study and Method of Fault Feature Dimension Reduction Based on Fuzzy Rough Set Combined with Outlier Detection

作　　者：姜万录[1,2] 杨凯[1,2] 朱勇[1,2] 郑直[1,2] 胡浩松[1,2] 

机构地区：[1]燕山大学河北省重型机械流体动力传输与控制重点实验室,河北秦皇岛066004 [2]先进锻压成形技术与科学教育部重点实验室(燕山大学),河北秦皇岛066004

出　　处：《机床与液压》2016年第7期155-160,共6页Machine Tool & Hydraulics

基　　金：国家自然科学基金资助项目(51475405);国家重点基础研究发展计划(973计划)资助项目(2014CB046405);河北省自然科学基金资助项目(E2013203161);河北省研究生创新资助项目(00302-6370002)

摘　　要：针对在模式识别过程中样本的特征维数过高,模糊粗糙集特征降维方法易受奇异点影响的问题,提出了一种基于奇异点检测和模糊粗糙集相结合的特征降维方法。该方法通过计算样本基于重心的不稳定系数值的变化,据此剔除奇异点,消除了奇异点对模糊粗糙集特征降维的影响,选择出对模式分类敏感的特征子集。通过仿真数据和齿轮故障数据进行实验分析,实验结果验证了所提出的特征降维方法的有效性。Aimed for the poblems of high feature dimensions of the samples in the process of pattern recognition and fuzzy rough set dimension reduction method easily affected by the outlier,a dimension reduction method based on fuzzy rough set combined with outlier detection was proposed. The method was eliminated of outlier by the calculation of instability coefficient values changing based on gravity among samples,which dispelling the influence of outliers disturbing the fuzzy rough set feature dimension reduction,and selected the feature set sensitive to pattern classification. The proposed method was tested by the simulation data and the gear failure data analysis. The experimental results show the effectiveness of the proposed feature dimension reduction method.

关 键 词：特征降维 模糊粗糙集 奇异点检测 故障识别 

分 类 号：TH17[机械工程—机械制造及自动化]