A process monitoring and quality prediction method based on combining kernel partial least squares (KPLS)
with Fisher discriminant analysis (FDA), hybrid KPLS-FDA, is proposed. Firstly, the nonlinear feature of process data is
extracted by using KPLS, the internal model of KPLS is established by using FDA, and the optimal feature vector and the
discriminant vector which satisfies maximal separation degree are obtained for condition monitoring. If the process is under
normal condition, the regression model of KPLS is further used for quality prediction. Otherwise, the similar degree in the
fault feature direction is used for fault diagnosis. Finally, the simulation research for steel rolling process is performed to
show its accuracy and effectiveness in fault diagnosis and quality prediction.