摘要
滚动轴承故障预测和健康管理(PHM)方法可以提取大量的故障特征数据,这些数据虽然有很大的潜在价值,但也存在高维、高冗余性的特点,难以直接分析和利用。因此,针对轴承故障特征数据的特点,以去除数据冗余性、筛选敏感特征为目的,提出两阶段特征选择算法。该方法的第1阶段采用拉普拉斯得分(LS)对原始特征按局部保持能力进行排序,利用互信息聚类算法删除特征集中的冗余特征。第2阶段采用多变量模式识别中的马田系统(MTS)方法对剩余特征进行综合评价,挖掘对故障分类更有效的特征。轴承退化仿真试验数据验证结果表明,提出的两阶段特征选择算法可以有效地去除冗余度、提高故障监测准确率,可以有效的运用到滚动轴承的初期故障检测中。
Rolling bearing prognostic and health management(PHM)method can extract a large number of fault characterization data.Those data are of great potential value,because of their characteristics of high-dimensionality and high-redundancy.However,direct analysis and utilization of them are impossible.Therefore,aiming at reducing the redundancy data and screening sensitive features,a two-stage feature selection algorithm is proposed.In the first stage of the method,the Laplacian score(LS)is used to sort the original features based on their locality preserving power,and the mutual information-based clustering algorithm is utilized to remove the redundant features of the original feature set.In the second stage,the Mahalanobis-Taguchi system(MTS),as a useful multivariate pattern recognition method,is employed to comprehensively evaluate the remaining features,unearthing features which are prone to fault classification.The verification results of the bearing degradation simulation test data show that the proposed two-stage feature selection algorithm can effectively remove redundancy and improve the accuracy of fault monitoring.This method can be effectively applied to the initial fault detection of rolling bearings.
作者
彭宅铭
程龙生
詹君
姚启峰
Peng Zhaiming;Cheng Longsheng;Zhan Jun;Yao Qifeng(School of Economics and Management,Nanjing University of Science&Technology,Nanjing 210094,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2020年第4期186-193,共8页
Journal of Electronic Measurement and Instrumentation
关键词
特征选择
拉普拉斯得分
互信息
马田系统
故障检测
feature selection
Laplacian score(LS)
mutual information
Mahalanobis-Taguchi system(MTS)
fault detection
