With increasing importance of batch processes

its monitoring and fault diagnosis technology has become a research focus. An envelope surface model of normal and fault operation conditions based on kernel Fisher analysis technique was proposed

and also an online fault diagnosis program was presented in this paper. With kernel Fisher discriminant analysis's characteristics for classification

envelope surface models for normal data and each fault data were built. Compared with multiway Fisher discriminant analysis (MFDA)

the proposed method unfolded the process data batch-wise

so it could deal with unequal batch length and did not require to predict the whole production trajectory when implementing online fault diagnosis. Also kernel function could deal with nonlinear problems. Finally

the proposed method was tested in the simulation platform of penicillin fermentation process. The simulation results comparing with the improved MFDA showed that the proposed method could diagnose the fault early

and also had self-learning ability for new fault.