Abstract A large Proportion of batch processes commonly have traits of non-Gaussian and nonlinear. In this work, Multiway Kernel Entropy Independent Component Analysis (MKEICA) algorithm was developed to formulate more accurate model for process monitoring so as to enhance the monitoring performance. The original process data with three-dimension were first expanded into two-dimensional data matrix by using AT variable expansion method. The Kernel Entropy Component Analysis (KECA) was then employed to preprocess the data in order to reduce data redundancy. Such approach can also retain the information of cluster structure and maximize the essential characteristics of data. After that, a monitoring model of MKEICA was established for production process monitoring. Once a fault is detected, a nonlinear contribution plots method would be utilized to diagnose the fault variables. Consequently, to illustrate the superiority and feasibility, the proposed method was conducted on the penicillin simulation platform and the actual pharmaceutical production process.

中文翻译：

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非高斯非线性共存微生物制药发酵过程故障诊断

摘要 大量的批处理过程普遍具有非高斯和非线性的特征。在这项工作中，开发了多路核熵独立分量分析（MKEICA）算法来制定更准确的过程监控模型，以提高监控性能。首先用AT变量展开法将原始的3维过程数据展开为二维数据矩阵。然后采用核熵分量分析（KECA）对数据进行预处理，以减少数据冗余。这种方法还可以保留集群结构的信息，最大限度地发挥数据的本质特征。之后，建立了MKEICA的监控模型，用于生产过程监控。一旦检测到故障，将利用非线性贡献图方法来诊断故障变量。因此，为了说明该方法的优越性和可行性，在青霉素模拟平台和实际药品生产过程中进行了实验。