Kernel principal component analysis (KPCA) is suitable for nonlinear process monitoring

but it suffers many limitations

such as great calculation load

and poor real time performance. A monitoring method based on sparse KPCA (SKPCA) was proposed to decrease calculation load and improve real time monitoring. SKPCA was firstly used to weight the normal modeling data

and the minority of data with high weight could basically represent the information of the whole data

so modeling data could be largely reduced. Following this

KPCA model and the monitoring indices were built based on the sparse modeling data. In the end

taking a chemical separation process for example

KPCA and SKPCA were compared in terms of monitoring result and real time performance

and the superiority of the proposed SKPCA method was demonstrated.