A feature subspace based kernel principal component analysis(KPCA)

method (FS_KPCA)and its application to fault detection and diagnosis are presented in this paper to overcome the shortcoming of the standard KPCA method which is not appropriate to deal with a large number of training data.FS_KPCA simplifies the kernel matrix and reduces the computational cost of KPCA by constructing a lower-dimensional orthonormal based on feature subspace.When applied to process monitoring

the FS_KPCA-based method is more efficient in computation and needs less computer memory than standard KPCA-based methods.Computer simulation of non-isothermal CSTR process monitoring demonstrates the effectiveness and efficiency of the proposed method.