Batch Process Monitoring and Fault Diagnosis Based on MKMFDA

In view of the characteristics of batch process and the defect of batch process monitoring method based on multiway principal component analysis (MPCA), using the advantage of kernel mapping in dealing with nonlinear process and the advantage of fisher discriminant analysis (FDA) in the ability of fault diagnosis, a novel batch performance monitoring and fault diagnosis method based on multi-model kernel multi-way FDA (MKMFDA) was proposed. The key to the proposed approach was to calculate the distance of block data which were projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set was used to perform fault diagnosis. The proposed method was applied to monitoring fed-batch penicillin production, and the results clearly showed that, in comparison to the moving window MPCA method, the proposed method was more accurate and efficient.