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基于核函数主元分析的机械设备状态识别
引用本文:李巍华,廖广兰,史铁林,杨叔子.基于核函数主元分析的机械设备状态识别[J].华中科技大学学报(自然科学版),2002,30(12):67-70.
作者姓名:李巍华  廖广兰  史铁林  杨叔子
作者单位:华中科技大学机械科学与工程学院
基金项目:国家重大基础研究专项基金资助项目 (G19980 2 0 32 0 ),湖北省自然科学基金资助项目 (2 0 0 0J12 5 )
摘    要:研究了核函数主元分析在机械故障模式分类中的应用,通过计算原始空间的内积核函数实现原始数据空间到高维数据空间的非线性映射,再对高维数据作主元分析,求取更易于分类的核函数主元,实验表明,核函数主元分析更适于提取故障信号的非线性特征,能有效区分不同的故障模式,可以应用于机械设备的状态识别。

关 键 词:状态识别  模式分类  特征提取  核函数主元分析
文章编号:1671-4512(2002)12-0067-04
修稿时间:2002年7月17日

Machine condition recognition based on kernel principal component analysis
Li Weihua,Liao Guanglan,Shi Tielin,Yang Shuzhi.Machine condition recognition based on kernel principal component analysis[J].JOURNAL OF HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY.NATURE SCIENCE,2002,30(12):67-70.
Authors:Li Weihua  Liao Guanglan  Shi Tielin  Yang Shuzhi
Institution:Li Weihua Liao Guanglan Shi Tielin Yang Shuzhi
Abstract:Describes a study of kernl Principal Component Analysis in Mechanical faults feature extraction and clessification. By using integral operator kernel functions, the nonlinear principal components in high dimensional feature spaces related to input space by some nonlinear map were computed. Industrial gearbox vibration signals measured from different operating conditions were analyzed using the above method. Experiment results indicate that kernel PCA has good performance in extracting the nonlinear feature from fault signals, and it is able to identify clearly a gearbox operating condition with fatigue spalling or wear compared with the normal condition. It can be applied in machine condition dynamic recognition.
Keywords:condition recognition  pattern classification  feature extraction  kernel principal component analysis  
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