Concurrent Canonical Correlation Analysis Modeling for Quality-Relevant Monitoring

Qinqin ZHU, Qiang LIU, S. Joe QIN

Research output: Journal PublicationsJournal Article (refereed)peer-review

28 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1044-1049
Number of pages6
Issue number7
Early online date9 Aug 2016
Publication statusPublished - 2016
Externally publishedYes


  • Concurrent Canonical Correlation Analysis (CCCA)
  • Quality-Relevant Monitoring


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