Online monitoring of nonlinear multivariate industrial processes using filtering KICA-PCA

In this paper, a novel approach for processes monitoring, termed as filtering kernel independent component analysis-principal component analysis (FKICA-PCA), is developed. In FKICA-PCA, first, a method to calculate the variance of independent component is proposed, which is significant to make Gaussian features and non-Gaussian features comparable and to select dominant components legitimately; second, Genetic Algorithm is used to determine the kernel parameter through minimizing false alarm rate and maximizing detection rate; furthermore, exponentially weighted moving average (EWMA) scheme is used to filter the monitoring indices of KICA-PCA to improve monitoring performance. In addition, a novel contribution analysis scheme is developed for FKICA-PCA to diagnosis faults. The feasibility and effectiveness of the proposed method are validated on the Tennessee Eastman (TE) process. © 2013 Elsevier Ltd.