To overcome the problem of batch process caused by the traditional process dynamics multistage characteristic

the multiphase auto regression-principal component analysis (AR-PCA) monitoring method is proposed based on affine propagation (AP) clustering optimized with a population diversity-based particle swarm optimization algorithm (PDPSO). The method introduced PDPSO method to improve the AP clustering. It avoided the blindness of common method that indirectly chose the preference based on the clustering evaluation index. Then we established the AR-PCA model for the data samples of the multiphase fermentation process to eliminate the dynamic characteristics of each stage and the auto-and-cross-correlation between variables. Finally

the PCA model is established for the residual of the AR model for fault monitoring of the batch process. The method is applied to the process of penicillin fermentation. Experiments show that the method can effectively divide the process into different phases and reduce the false and leak alarms.