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Fault monitoring of multiphase batch process is a difficult problem in multivariate statistical process monitoring. It needs to consider not only the process monitoring under stable mode, but also the transition mode with strong dynamic nonlinearity. Since the data has different correlations under different operating modes, it is necessary to establish different monitoring models for each process mode, especially the transition process between stable modes. The biggest feature is the dynamic characteristics of the variables. This feature can be better reflected in this transition using a time-varying covariance instead of a fixed covariance during the transition phase. In this paper, a new strategy for batch process sub-phase partition and process monitoring is proposed. Firstly, the three-dimensional data matrix is expanded into a new two-dimensional data according to the time slice expansion strategy. Secondly, the data of each time slice is transformed by Kernel Entropy Component Analysis (KECA), and then the production process is divided into phases according to the spatial angle of the kernel entropy. The production operation process is divided into a stable phase and a transition phase, and monitoring models are respectively established to monitor the production process; Finally, the application of the penicillin fermentation simulation platform shows that the Sub-MKECA phase partition results can reflect the mechanism of the batch process well, and the fault monitoring of the process shows that it can detect faults in time and accurately, and has high practicality value.

Phase Partition and Fault Diagnosis of Batch Process Based on KECA Angular Similarity