An inseparability metric to identify a small number of key variables for improved process monitoring

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 variables is fault specific. In this paper, we propose a metric to identify the key variables of a fault. The metric measures the extent of inseparability in the subspace of a variable subset and thus, provides a reasonable estimate of the monitoring performance for a subset of variables. The excellent ability of the proposed metric in identifying the right key variables is demonstrated through the benchmark Tennessee Eastman Challenge problem.