In order to research the application limitations of principal component analysis fault diagnose model in non-linear time-varying and reduce computational complexity for process monitoring based on non-linear principal component

kernel transformation theory is introduced into the data feature extraction of nonlinear space and a fault monitoring model based on kernel principal component analysis（ KPCA） for constant value detection has also been proposed. Through the proper selection of kernel function parameter values

the KPCA model can achieve constant value of process fault detection and have lower computational complexity than other non-linear algorithms. The fault detection experiment for beer fermentation process shows that this method is able to detect process faults in a timely manner and have real-time performance and accuracy in the batch process of slowly time-varying.