Conventional independent component analysis (ICA) monitoring methods extract the feature information of process data by selecting more important independent components (ICs), which discard a small part of ICs that may contain useful information for faults, leading to unsatisfactory monitoring results. However, when the number of sampling points is greater than that of process variables, the ICA monitoring model does not work well. To address the aforementioned problems, a novel monitoring method, multiphase enhanced high‐order information extraction (MEHOIE), is proposed in this paper. The entire production process was first divided into several steady phases and transition phases by the affinity propagation (AP) phase partitioning method. The enhanced high‐order information extraction (EHOIE) model was then built in each phase for fault monitoring. Finally, the algorithm was applied in the penicillin simulation platform and industrial microbial pharmaceutical process. The flexibility and superiority of this algorithm were verified by comparing it with other conventional methods.

中文翻译：

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增强的高阶信息提取，用于多阶段批处理过程故障监控

常规的独立组件分析（ICA）监视方法通过选择更重要的独立组件（IC）来提取过程数据的特征信息，从而丢弃了可能包含故障有用信息的一小部分IC，从而导致监视结果不尽人意。但是，当采样点的数量大于过程变量的数量时，ICA监视模型将无法正常工作。为了解决上述问题，本文提出了一种新的监测方法，即多相增强高阶信息提取（MEHOIE）。首先，通过亲和力传播（AP）相分配方法将整个生产过程分为几个稳定阶段和过渡阶段。然后，在每个阶段都构建了增强的高级信息提取（EHOIE）模​​型以进行故障监视。最后，将该算法应用于青霉素模拟平台和工业微生物制药过程。通过与其他常规方法进行比较，验证了该算法的灵活性和优越性。