基于主元分析残差空间的自适应统计方法及其应用

摘要

An adaptive statistical method in the residual space of principal component analysis (PCA)-based is proposed in this paper.First,the original space is separated into principal component space and residual space.Next,in residual space,an adaptive statistic variable,Te2,which is the combination of Hotelling's T2 and Euclidean distance,is given.The proposed method,PCA-Q and kernal pricipal component analysis(KPCA)-Q are applied to the Tennessee Eastman (TE) process.The comparison of monitoring results shows that the proposed method is superior to PCA-Q and KPCA-Q.%提出一种基于主元分析(PCA)残差空间的自适应统计方法.将原始空间分解为主元空间和残差空间,然后根据残差空间得分变量方差变化趋势,给出由Hotelling's T2和欧式距离相结合的自适应统计量Te2.将PCA-T2、PCA-Q以及核主元分析(KPCA)-Q等方法应用于田纳西-伊斯曼(TE)过程中,仿真结果表明PCA-Te2具有较高的检测性能.