An ensemble approach of fault diagnosis based on lifting wavelet, multi-way principal component analysis and support vector machines (LW-MPCA-SVM) for batch process is developed in this paper. Firstly, data are preprocessed to remove noise by lifting scheme wavelets, which are faster than first generation wavelets; then multi-way principal component analysis is used to extract feature for high-accurate diagnosis of the batch process, and SVM is used to diagnose faults. To validate the performance and effectiveness of the proposed scheme, LW-MPCA-SVM is applied to diagnose the faults in the simulation benchmark of fed-batch penicillin fermentation process. The results of simulation clearly demonstrate the effectiveness and feasibility of the proposed method, which diagnoses faults more accurately with desirable reliability.