Multiphase batch process monitoring based on higher‐order cumulant analysis

Abstract

AbstractIn this paper, a two‐step phase partitioning strategy is proposed. Firstly, the number of phases is automatically determined according to the intra‐class and inter‐class similarity of feature space data, thus avoiding excessive manual intervention. Secondly, the phases are partitioned by step‐wise adding the kernel entropy extended load matrix (KEELM), avoiding the wrong division of phases caused by unstable state of working condition conversion. A process monitoring model based on multiway kernel entropy independent component analysis (MKEICA) is constructed in each sub‐phase to deal with complex batch processes with nonlinear and non‐Gaussian properties. A new statistics index based on the idea of high order cumulant analysis (HCA) is constructed in each sub‐phase for process monitoring. Compared with the traditional second‐order statistics, it can obtain high‐order statistical information. Finally, the proposed method is applied to the penicillin simulation platform process and compared with the traditional multiway kernel independent components analysis (MKICA) and HCA methods to verify the effectiveness of the method that is mentioned above.