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Multivariate industrial process monitoring based on the integration method of canonical variate analysis and independent component analysis

机译：基于经典变量分析和独立成分分析相结合的多变量工业过程监控

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摘要

Tennessee Eastman (TE) process is a typical multivariate chemical process. It has some characteristics of complexity and nonlinearity. Therefore, it is an ideal research platform substituted for the real industrial process whose data is difficult to be achieved. Many scholars have done a lot of studies on monitoring approaches and applied these methods on the platform. However, it is not an easy work to obtain some ideal simulation results on detecting some special faults in TE process, such as the fault 3. In this paper, an integration of canonical variate analysis and independent component analysis method (CV-ICA) is proposed. It combines the advantages of canonical variate analysis (CVA) and independent component analysis (ICA) to solve these problems. CV-ICA applies CVA to calculate the canonical variates from the process data, and then employs ICA to extract independent components (ICs). The monitoring simulation demonstrates the availability of the proposed method.
机译：田纳西州伊士曼（TE）工艺是典型的多元化学工艺。它具有复杂性和非线性的特征。因此，它是替代难以获得数据的实际工业过程的理想研究平台。许多学者对监视方法进行了大量研究，并将这些方法应用于平台。然而，要获得理想的仿真结果以检测TE过程中的某些特殊故障（例如故障3）并非易事。本文将规范变量分析和独立分量分析方法（CV-ICA）集成在一起。建议。它结合了规范变量分析（CVA）和独立成分分析（ICA）的优点来解决这些问题。 CV-ICA应用CVA从过程数据中计算规范变量，然后使用ICA提取独立的组件（IC）。监控仿真表明了该方法的有效性。

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