Parallel PCA–KPCA for nonlinear process monitoring
Q Jiang, X Yan - Control Engineering Practice, 2018 - Elsevier
Both linear and nonlinear relationships may exist among process variables, and monitoring
a process with such complex relationships among variables is imperative. However,
individual principal component analysis (PCA) or kernel PCA (KPCA) may not be able to
characterize these complex relationships well. This paper proposes a parallel PCA–KPCA
(P-PCA–KPCA) modeling and monitoring scheme that incorporates randomized algorithm
(RA) and genetic algorithm (GA) for efficient fault detection for a process with linearly …
a process with such complex relationships among variables is imperative. However,
individual principal component analysis (PCA) or kernel PCA (KPCA) may not be able to
characterize these complex relationships well. This paper proposes a parallel PCA–KPCA
(P-PCA–KPCA) modeling and monitoring scheme that incorporates randomized algorithm
(RA) and genetic algorithm (GA) for efficient fault detection for a process with linearly …