Detective variables of industrial processes show nonlinear and non-Gaussian behavior.This paper proposes the kernel principal component analysis (WKPCA）based on wavelet kernel clustering to handle the nonlinearity of the process

and introduces support vector data description (SVDD）to model the process.The first step is to construct the kernel function by using Morlet wavelet because of its advantages of multi-resolution analysis and good fitness

which could enhance nonlinear mapping and anti-noise capability of the kernel function.Then this method uses cluster analysis in the feature space

and chooses the data which represent the characteristic center in every cluster

which could decrease calculation load of the kernel function.Finally the method uses the monitor statistics offered by SVDD to describe the non-Gaussian information.Application to the Tennessee-Eastman benchmark process showed effectiveness and accuracy of detecting fault and exception generated by the system.