On-Line Batch Process Monitoring Using Multiway Kernel Independent Component Analysis

  • Fei Liu
  • Zhong-Gai Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


For on-line batch process monitoring, multiway principal component analysis (MPCA) is a useful tool. But the MPCA-based methods suffer two disadvantages: (i) it restricts itself to a linear setting, where high-order statistical information is discarded; (ii) all the measurement variables must follow Gaussian distribution and the objective of MPCA is only to decorrelate variables, but not to make them independent. To improve the ability of batch process monitoring, this paper proposes a monitoring method named multiway kernel independent component analysis (MKICA). By using kernel trick, the new monitoring indices are investigated, which have been mapped into high-dimensional feature space. On the benchmark simulator of fed-batch penicillin production, the presented method has been validated.


Batch Process Kernel Principal Component Analysis Kernel Trick Monitoring Index Nonlinear Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fei Liu
    • 1
  • Zhong-Gai Zhao
    • 1
  1. 1.Institute of AutomationSouthern Yangtze UniversityWuxiP.R. China

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