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中南大学学报(自然科学版)

Journal of Central South University

第47卷    第5期    总第261期    2016年5月

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文章编号:1672-7207(2016)05-1559-06
基于半监督PCA-LPP流形学习算法的故障降维辨识
张晓涛,唐力伟,王平,邓士杰

(军械工程学院 火炮工程系,河北 石家庄,050003)

摘 要: 提出一种基于半监督思想PCA-LPP的流形学习维数约简故障辨识方法,兼顾PCA的全局结构和LPP的局部结构保持以及样本的类别信息,构造新的投影矩阵目标函数,给出PCA-LPP流形学习算法的计算原理。采用UCI中wine数据集验证半监督PCA-LPP方法的维数约简性能,并就齿轮箱故障声发射实验信号,以小波包能量熵作为特征向量,并将特征向量的降维结果输入支持向量机进行故障类型辨识。研究结果表明:半监督PCA-LPP方法的降维结果,能够充分考虑不同故障特征向量的差异信息,相应的故障类型辨识精度高于PCA及LPP方法。

 

关键字: 流形学习;局部保持投影;主元分析;故障诊断;故障辨识

Fault identification and dimensionality reduction method based on semi-supervised PCA-LPP manifold learning algorithm
ZHANG Xiaotao, TANG Liwei, WANG Ping, DENG Shijie

First Department, Ordnance Engineering College, Shijiazhuang 050003, China

Abstract:A novel fault identification dimensionality reduction method based on semi-supervised PCA-LPP manifold learning algorithm was 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, and the calculation principle of semi-supervised PCA-LPP manifold learning algorithm was given. The processing results of wine dataset of UCI show that the semi-supervised PCA-LPP method has a good ability of dimensionality reduction. Aiming at the gearbox acoustic emission signals, its eigenvectors is constructed by wavelet packet energy entropy, and the dimensionality reduction results of eigenvectors are given to the support vector machine, the fault identification of semi-supervised PCA-LPP method obtains higher identification rate than that of LPP and PCA, because the method considers the similarities and differences between all eigenvectors.

 

Key words: manifold learning; locality preserving projection; principal component analysis; fault diagnosis; pattern recognition

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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