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- Title
FAULT DETECTION OF NON-LINEAR PROCESSES USING KERNEL INDEPENDENT COMPONENT ANALYSIS.
- Authors
Jong-Min Lee; Qin, S. Joe; In-Beum Lee
- Abstract
In this paper, a new non-linear process monitoring method based on kernel independent component analysis (KICA) is developed. Its basic idea is to use KICA to extract some dominant independent components capturing non-linearity from normal operating process data and to combine them with statistical process monitoring techniques. The proposed method is applied to the fault detection in the Tennessee Eastman process and is compared with PCA, modified ICA, and KPCA. The proposed approach effectively captures the non-linear relationship in the process variables and showed superior fault detectability compared to other methods while attaining comparable false alarm rates.
- Subjects
NONLINEAR systems; FALSE alarms; SYSTEMS theory; ERRORS; METHODOLOGY; RESEARCH
- Publication
Canadian Journal of Chemical Engineering, 2007, Vol 85, Issue 4, p526
- ISSN
0008-4034
- Publication type
Article
- DOI
10.1002/cjce.5450850414