中南大学学报（自然科学版）2016，Vol.47Issue(5)：1559-1564,6.DOI:10.11817/j.issn.1672-7207.2016.05.015
基于半监督PCA-LPP流形学习算法的故障降维辨识
Fault identification and dimensionality reduction method based on semi-supervised PCA-LPP manifold learning algorithm
摘要
Abstract
A novel fault identification dimensionality reduction method based on semi-supervised PCA-LPP manifold learning algorithmwas proposed. The objective function of projection matrix of semi-supervised PCA-LPP was constructed by global structure and local structure,the global structure was described by PCA, the local structure described by LPP and category information of samples,andthe calculation principle of semi-supervised PCA-LPP manifold learning algorithm was given.The processing results of wine dataset of UCI show thatthesemi-supervised PCA-LPP method has a good ability of dimensionality reduction.Aiming at the gearbox acoustic emission signals, its eigenvectorsisconstructed by wavelet packet energy entropy, andthedimensionality reduction results of eigenvectorsare giventothe support vector machine, the fault identification of semi-supervised PCA-LPP method obtainshigher identification rate thanthat ofLPP and PCA, because the method considersthe similarities and differences between all eigenvectors.关键词
流形学习/局部保持投影/主元分析/故障诊断/故障辨识Key words
manifold learning/locality preserving projection/principal component analysis/fault diagnosis/pattern recognition分类
机械制造引用本文复制引用
张晓涛,唐力伟,王平,邓士杰..基于半监督PCA-LPP流形学习算法的故障降维辨识[J].中南大学学报（自然科学版）,2016,47(5):1559-1564,6.基金项目
国家自然科学基金资助项目(50775219)；军队科研资助项目([2011]107)(Project(50775219) supported by the National Natural Science Foundation of China （50775219）
Project([2011]107) supported by the Military Research Foundation) （[2011]107）
