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Online monitoring of nonlinear multivariate industrial processes using filtering KICA–PCA.
- Source :
-
Control Engineering Practice . Jan2014, Vol. 22, p205-216. 12p. - Publication Year :
- 2014
-
Abstract
- Abstract: 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 09670661
- Volume :
- 22
- Database :
- Academic Search Index
- Journal :
- Control Engineering Practice
- Publication Type :
- Academic Journal
- Accession number :
- 92729601
- Full Text :
- https://doi.org/10.1016/j.conengprac.2013.06.017