关键词: multivariate statistical process monitoring;principal component analysis;independent component analysis;kernel density estimation;polypropylene;catalyzer reactor;fault detection;data-driven tools, data-driven tools

Abstract: Data-driven tools, such as principal component analysis (PCA) and independent component
analysis (ICA) have been applied to different benchmarks as process monitoring methods. The
difference between the two methods is that the components of PCA are still dependent while
ICA has no orthogonality constraint and its latent variables are independent. Process
monitoring with PCA often supposes that process data or principal components is Gaussian
distribution. However, this kind of constraint cannot be satisfied by several practical
processes. To extend the use of PCA, a nonparametric method is added to PCA to overcome the
difficulty, and kernel density estimation (KDE) is rather a good choice. Though ICA is
based on non-Gaussian distribution information, KDE can help in the close monitoring of the
data. Methods, such as PCA, ICA, PCA with KDE (KPCA), and ICA with KDE (KICA), are
demonstrated and compared by applying them to a practical industrial Spheripol craft
polypropylene catalyzer reactor instead of a laboratory emulator.

Key words: multivariate statistical process monitoring, principal component analysis, independent component analysis, kernel density estimation, polypropylene, catalyzer reactor