Abstract: Numerous statistical process monitoring methods based on principal component analysis (PCA) have been developed and applied to various chemical processes for fault detection and identification.Moving principal component analysis (MPCA) is one of the improved statistical process monitoring methods based on PCA.The change in the subspace spanned by some selected principal components is monitored for fault detection in MPCA.However, PCA-based monitoring methods are linear techniques and have been proved inefficient and problematic for nonlinear systems.This paper presents a novel fault detection method based on kernel principal angle (KPA), which is efficient for nonlinear systems.Constructing feature subspace and computing the kernel principal angel are two main parts in the proposed method.That is, the basic idea of the KPA-based detection method is similar to that of MPCA.The performance of the proposed fault detection method was compared with the conventional multivariate statistical process control (cMSPC) and MPCA in the application to simulated data obtained from the Tennessee Eastman (TE) process.The results clearly showed that the performance of the KPA-based fault detection method was considerably better than that of the other two.

摘要： 基于主元分析（PCA）的统计检测方法已经被广泛应用于各种化工过程的故障检测和识别.移动主元分析（moving principal component analysis，简称MPCA）算法基于PCA，根据主元子空间的变化来判断故障是否发生.然而，基于主元分析的统计检测方法是线性方法，无法有效应用于非线性系统.因此，提出一种适合于非线性系统的故障检测方法——基于核主角(kernel principal angle，简称KPA)的故障检测方法，其基本思想与MPCA相似，主要内容包括构建特征子空间和核主角测量两部分.TE过程故障检测仿真实验证明，基于核主角的故障检测方法优于传统的多元统计检测方法（cMSPC）和MPCA.