期刊
CHEMICAL ENGINEERING RESEARCH & DESIGN
卷 88, 期 4A, 页码 403-414出版社
ELSEVIER
DOI: 10.1016/j.cherd.2009.09.002
关键词
Process monitoring; Fault detection; Kernel independent component analysis; Kernel density estimation
资金
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Minstry of Education
- Shanghai Leading Academic Discipline Project [B504]
An approach for multivariate statistical monitoring based on kernel independent component analysis (Kernel ICA) is presented. Different from the recently developed KICA which means kernel principal component analysis (KPCA) plus independent component analysis (ICA), Kernel ICA is an improvement of ICA and uses contrast functions based on canonical correlations in a reproducing kernel Hilbert space. The basic idea is to use Kernel ICA to extract independent components and later to provide enhanced monitoring of multivariate processes. I-2 (the sum of the squared independent scores) and squared prediction error (SPE) are adopted as statistical quantities. Besides, kernel density estimation (KDE) is described to calculate the confidence limits. The proposed monitoring method is applied to fault detection in the simulation benchmark of the wastewater treatment process and the Tennessee Eastman process, the simulation results clearly show the advantages of Kernel ICA monitoring in comparison to ICA and KICA monitoring. (C) 2009 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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