Abstract
Abstract In this paper, a new statistical process monitoring algorithm is proposed for detecting the process change influenced by a small shift in process variables, which is based on the multivariate exponentially weighted moving average (MEWMA) monitoring concept with independent component analysis (ICA) and kernel density estimation. The proposed monitoring method is applied to fault detection in the simulation benchmark of the biological wastewater treatment process (WWTP). For a small shift in these processes, the simulation results illustrated the monitoring power of MEWMA-ICA versus existing methods of the conventional PCA, ICA, MEWMA-PCA monitoring.
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