中国机械工程2017，Vol.28Issue(21)：2552-2556,5.DOI:10.3969/j.issn.1004-132X.2017.21.006
基于Schur分解和正交邻域保持嵌入算法的故障数据集降维方法
Fault Data Set Dimension Reduction Method Based on Schur Decomposition and ONPE Algorithm
摘要
Abstract
Aiming at dimension reduction of fault data set,a novel method in dimension reduction was proposed based on the combination of Schur decomposition and ONPE algorithm.Firstly wavelet packet decomposition was used to extract the fault signals of different frequency band energy features,then Schur decomposition and ONPE algorithm were used to project the high-dimensional data sets to lower dimensions.After the transformation,the considered pairwise samples within the same class were as close as possible,while those between classes were as far as possible.Finally,the lower dimension was collected and the K nearest neighbor classifier was input to recognize the different patterns.The fault characteristic data sets from a double span rotor test-rig were used to validate the proposed algorithm performances.The results show that this method may solve the problems of reducing the dimension of rotor fault features sets effectively.关键词
故障诊断/数据降维/Schur分解/正交邻域保持嵌入算法Key words
fault diagnosis/data dimension reduction/Schur decomposition/orthogonal neighborhood preserving embedding(ONPE) algorithm分类
机械制造引用本文复制引用
刘韵佳,赵荣珍,王雪冬..基于Schur分解和正交邻域保持嵌入算法的故障数据集降维方法[J].中国机械工程,2017,28(21):2552-2556,5.基金项目
国家自然科学基金资助项目(51675253) （51675253）
