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

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• 作者：Jicong Fan ; S. Joe Qin ; Youqing Wang
• 关键词：Process monitoring ; KICA&ndash ; PCA ; Variance of independent component ; EWMA ; Variable contribution analysis ; TE process
• 刊名：Control Engineering Practice
• 出版年：January, 2014
• 年：2014
• 卷：22
• 期：Complete
• 页码：205-216
• 全文大小：3741 K

文摘

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.