自动化学报2017，Vol.43Issue(4)：560-567,8.DOI:10.16383/j.aas.2017.c160317
全局与局部判别信息融合的转子故障数据集降维方法研究
A Method of Dimension Reduction of Rotor Faults Data Set Based on Fusion of Global and Local Discriminant Information
摘要
Abstract
Aimed at the problem that traditional dimension reduction methods cannot juggle global feature information and local discriminant information,a method of dimension reduction of the rotor fault dataset based on kernel principal component analysis (KPCA) and orthogonal locality sensitive discriminant analysis (OLSDA) is proposed.Firstly,the KPCA algorithm can reduce the correlation and redundant attributes of datasets and retain maximized original data information of global nonlinearity.Then,the OLSDA algorithm is used to fully excavate local manifold structure information of the data so as to extract the low-dimension essential feature with high discrimination.The method avoids distortion of local subspace structure by using a simultaneous orthogonalization process,and shows low dimensional results intuitively with 3-dimensional figure.Finally,the indexes to measure the dimension reduction effect are the recognition rate at which low-dimensional feature subset is input into KNN (K-nearest neighbor),the between-class scatter Sb and within-class scatter Sw of clustering analysis.Rotor experiment shows that this method can comprehensively extract global and local discriminant information,which makes classification of faults more clear and corresponding recognition accuracy rate significantly improved.This study provides a theoretical base for solving the visualization and classification problem of high-dimensional and nonlinear mechanical fault dataset.关键词
故障诊断/数据可视化/数据降维/核主元分析/正交化局部敏感判别分析Key words
Fault diagnosis/data visualization/data dimension reduction/kernel principal component analysis (KPCA)/orthogonal locality sensitive discriminant analysis (OLSDA)引用本文复制引用
赵孝礼,赵荣珍..全局与局部判别信息融合的转子故障数据集降维方法研究[J].自动化学报,2017,43(4):560-567,8.基金项目
国家自然科学基金(51675253),教育部高等学校博士学科点专项科研基金(20136201110004)资助 （51675253）
Supported by National Natural Science Foundation of China(51675253) and the Doctor Science Research Foundation of the Education Ministry of China (20136201110004) （51675253）
