The data collected from the chemical process reflect the process change and the state of the system

and the data-driven statistical method can efficiently monitor the process. However

for some complicated cases in industrial chemical and biological processes

the conventional linear statistical methods have poor ability of monitoring those processes

because the correlation between the process variables show particularly nonlinear characteristics. In this paper

a nonlinear statistical process monitoring and fault diagnosis method based on kernel Fisher discriminant analysis (KFDA)was proposed. The basic idea of KFDA is to first map the original space into a high dimension feature space via nonlinear mapping and then extract the optimal Fisher feature vector and discriminant vector to achieve process monitoring and fault diagnosis. The proposed method can effectively capture the nonlinear relationship in process variables. It was evaluated by the application to the fluid catalytic cracking unit (FCCU) model and its effectiveness was demonstrated.