期刊
CONTROL ENGINEERING PRACTICE
卷 22, 期 -, 页码 205-216出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2013.06.017
关键词
Process monitoring; KICA-PCA; Variance of independent component; EWMA; Variable contribution analysis; TE process
资金
- National Natural Science Foundation of China [61074081]
- Fundamental Research Funds for the Central Universities [RC1101]
- Doctoral Fund of Ministry of Education of China [20100010120011]
- Beijing Nova Program [2011025]
- Fok Ying-Tong Education Foundation [131060]
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. (C) 2013 Elsevier Ltd. All rights reserved.
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