For the multiphase property inherent in the batch process

and in order to overcome the serious deficiency that the traditional stage partition method divided the sampling points into several categories strictly according to the sampling time sequence and cannot make it look for the clustering center with the most similar data characteristics

a novel subset-MPCA method based on affinity propagation (AP) clustering is proposed. Using a new idea of random order clustering

this method disrupts the order of the time slice and AP is used to cluster with the random order. Thus

each data point can break the restriction of the time sequence and find the clustering center which has the most similar data characteristics with it

obtaining clustering subset and establishing the precise model. For online monitoring

information transmission is introduced to determine the stage attribution of real time sampling points to solve the problem of optimal model selection for unequal length batch. Experiments on penicillin simulation data show that this method can effectively reduce the leaking alarms and nuisance alarms than the traditional method

having more reliable monitoring performance.