期刊
IFAC PAPERSONLINE
卷 49, 期 7, 页码 1044-1049出版社
ELSEVIER
DOI: 10.1016/j.ifacol.2016.07.340
关键词
Concurrent Canonical Correlation Analysis (CCCA); Quality-Relevant Monitoring
资金
- Natural Science Foundation of China [61304107, 61490704, 61573022]
- China Postdoctoral Science Foundation [2013M541242]
- International Postdoctoral Exchange Fellowship Program [20130020]
- Fundamental Research Funds for the Central Universities [N130108001]
Canonical correlation analysis (CCA) is a well-known data analysis technique that extracts multidimensional correlation structure between two groups of variables. Due to the advantages of CCA on quality prediction, CCA-based modeling and monitoring are discussed in this paper. To overcome the shortcoming of CCA that focuses on correlation but ignores variance information, a new concurrent CCA (CCCA) modeling method is proposed to completely decompose the input and output spaces into five subspaces, to retain the CCA efficiency in predicting the output while exploiting the variance structure for process monitoring using subsequent principal component decomposition in the input and output spaces, respectively. The corresponding monitoring statistics and control limits are then developed in these subspaces. The Tennessee Eastman process is used to demonstrate the effectiveness of CCCA-based monitoring methods. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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