摘要
核主元分析在应用过程中,通常采用累积贡献率法确定核主元个数,舍弃一些贡献率较小的核主元,导致数据样本部分信息的损失,影响故障诊断的效果。针对这一情况,提出一种类均值核主元分析法,它将输入空间的数据样本映射到高维特征空间后,先求出各类映射数据的类均值矢量,然后在类均值矢量张成的子空间上对类均值矢量进行主元分析,利用构建的类均值核矩阵,建立类均值核主元算法。由类均值核主元形成的特征矢量包含原数据样本的全部变异信息,并且维数低于故障类别数,能够在类均值矢量基础上实现无信息损失的数据降维。将改进算法应用于滚动轴承故障诊断,结果表明,它具有比传统核主元分析更强的综合原始变量信息的能力,能更好地提取数据样本的类别信息,快速实现故障模式的准确识别。
In the application of kernel principal component analysis, cumulative contribution rate method is used to determine the number of kernel principal component usually, which abandon some kernel principal components whose contribution rate is small. It loses part information of samples and influences fault diagnosis effect. Aiming at this fact, a kernel principal component analysis method based on class mean is proposed. After data samples in input space are mapped into higher-dimensional space, class mean vectors of mapped data are determined, and then the PCA method is used to analyze the class mean vectors in the subspace of class mean vectors. Construct class mean kernel matrix, and make use of it to construct algorithm of class mean kernel principal component. The feature vectors formed by class mean kernel principal component include all variable information of initial data and its dimension is lower than the number of fault category. It can realize dimensionality reduction without information loss based on class mean vector. The improved algorithm is applied to rolling bearing fault diagnosis, and the results show that it has the stronger ability of integrating original variable information than KPCA, which can extract classified information of data samples more effectively and recoznize fault accurately.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2014年第3期123-129,共7页
Journal of Mechanical Engineering
基金
国家自然科学基金(51105138)
国家高技术研究发展计划(863计划
2012AA041805)
湖南省教育厅(11A034)资助项目
关键词
核主元分析
类均值核主元分析
故障诊断
kernel principal component analysis
class mean kernel principal component analysis
fault diagnosis
