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.