Improved PCA-SVDD based monitoring method for nonlinear process

@article{Shen2013ImprovedPB,
  title={Improved PCA-SVDD based monitoring method for nonlinear process},
  author={Feifan Shen and Zhihuan Song and Le Zhou},
  journal={2013 25th Chinese Control and Decision Conference (CCDC)},
  year={2013},
  pages={4330-4336}
}
Conventional principal component analysis (PCA) is limited to Gaussian process data due to its monitoring statistics. This paper introduces an improved PCA based method for nonlinear process monitoring using support vector data description (SVDD) by constructing two new monitoring statistics. Different from the traditional PCA method, monitoring statistics based on SVDD model have no Gaussian assumption. Thus the new monitoring statistics have no restriction to the distribution of process data… CONTINUE READING

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