Abstract
To handle the nonlinear feature in the industry process, this paper combines partial least squares (PLS) and neural component analysis (NCA), named as NCA-PLS. Different from NCA, the principal components are selected based on the correlation coefficient with KPI variables rather than the variance. As such, by redesigning the PCs extraction mechanism, NCA-PLS can successfully extract the KPI-related components from the process data and use them for process monitoring.
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