Abstract:In a large-scale complex chemical process, hundreds of variables are measured. Since statistical process monitoring techniques such as PCA typically involve dimensionality reduction, all measured variables are often provided as input without pre-selection of variables. In our previous work [1], we demonstrated that reduced models based on only a small number of important variables, called key variables, which contain useful information about a fault, can significantly improve performance. This set of key varia… Show more
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