Modified Independent Component Analysis for Multivariate Statistical Process Monitoring

Abstract In this paper, a modified independent component analysis (ICA) and its application to process monitoring are proposed. The basic idea of this approach is to use the modified ICA to extract some dominant independent components from normal operating process data and to combine them with statistical process monitoring techniques. The proposed monitoring method is applied to fault detection and identification in the Tennessee Eastman process and is compared with the conventional PCA based monitoring method. The monitoring results demonstrate that the proposed method outperforms PCA in terms of the fault detection rate while attaining comparable false alarm rate.