A new dissimilarity method integrating multidimensional mutual information and independent component analysis for non-Gaussian dynamic process monitoring

Traditional multivariate statistical processes monitoring (MSPM) techniques like principal component analysis (PCA) and partial least squares (PLS) are not well-suited in monitoring non-Gaussian processes because the derivation of T2 and SPE indices requires the approximate multivariate Gaussian distribution of the process data. In this paper, a novel pattern analysis driven dissimilarity approach is developed by integrating multidimensional mutual information (MMI) with independent component analysis (ICA) in order to quantitatively evaluate the statistical dependency between the independent component subspaces of the normal benchmark and monitored data sets. The new MMI based ICA dissimilarity index is derived from the higher-order statistics so that the non-Gaussian process features can be extracted efficiently. Moreover, the moving-window strategy is used to deal with process dynamics. The multidimensional mutual information based ICA dissimilarity method is applied to the Tennessee Eastman Chemical process. The process monitoring results of the proposed method are demonstrated to be superior to those of the regular PCA, PCA dissimilarity, regular ICA and angle based ICA dissimilarity approaches.

► A new dissimilarity method is developed for process monitoring.
► Integrating multidimensional mutual information with independent component analysis.
► Higher-order statistics to handle process non-Gaussianity.
► Better fault detection performance than ICA and angle based dissimilarity methods.