Slow feature analysis (SFA) is an unsupervised liner learning algorithm and lacks the ability to consider class label information and data nonlinearity. In order to solve this problem

a novel nonlinear process fault detection method is proposed based on kernel slow feature discriminant analysis and support vector data description (KSFDA-SVDD). Firstly

process data is mapped from the original space into a high dimension feature space via kernel trick. Then

the discriminant matrix that maximizes the temporal variation of between-class pseudo-time series and minimizes the temporal variation of within-class pseudo-time series simultaneously is calculated. Finally

SVDD is applied to describe the distribution region of normal operation data which is projected to the discriminant matrix and one monitoring index is constructed to indicate the occurrence of the abnormal event. Simulation results on the continuous stirring tank reactor (CSTR) process show that the proposed method is more effective than the traditional KPCA method in terms of detecting faults.