期刊
CHINESE JOURNAL OF CHEMICAL ENGINEERING
卷 22, 期 11-12, 页码 1243-1253出版社
CHEMICAL INDUSTRY PRESS CO LTD
DOI: 10.1016/j.cjche.2014.09.021
关键词
Process monitoring; Independent component analysis; Kernel trick; Time structure; Fault identification
资金
- National Natural Science Foundation of China [61273160]
- Natural Science Foundation of Shandong Province of China [ZR2011FM014]
- Shandong Province [BS2012ZZ011]
- China University of Petroleum [CX2013060]
Kernel independent component analysis (KICA) is a newly emerging nonlinear process monitoring method, which can extract mutually independent latent variables called independent components (ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis (KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature. Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA. (C) 2014 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.
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