期刊
ISA TRANSACTIONS
卷 73, 期 -, 页码 257-267出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2018.01.003
关键词
Nonlinear plant-wide process; Quality-related monitoring; Mutual information; Multi-block kernel principal component analysis; Support vector data description
资金
- 973 Project of China [2013CB733600]
- Fundamental Research Funds for the Central Universities [222201717006]
- Program of Introducing Talents of Discipline to Universities (the 111 Project) [B17017]
Due to prior knowledge being often unavailable in practice, a multi-block strategy totally based on data driven analytics is an appropriate alternative for plant-wide processes. However, most recent multi-block methods are relatively vague or insufficient for dividing up the process space and lack the comprehensive fault information for quality-related monitoring. This work intends to develop a more reasonable multi block method and demonstrate the negative impacts of quality-unrelated variables. Both motivations are entirely dependent on the correlation between variables. A major innovation is to determine those independent or related sets of variables, and to provide a more precise indication for those quality related faults. Sub-blocks with related variables are each modeled by the KPCA, and the rest of the independent variables are treated as an input for a SVDD model. Finally, all of the statistical indicators are aggregated into a single statistic through Bayesian inference. The benefits of the proposed multi-block scheme (MKPCA-SVDD) are elaborated on in detail using numerical simulation, TE benchmark and industrial p-xylene oxidation process. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.
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