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A New Method for Process Monitoring Based on Mixture Probabilistic Principal Component Analysis Models

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

Abstract

Conventional PCA-based monitoring method relies on the assumption that process data is normally distributed, which the actual industrial processes often don’t satisfy. Instead, mixture probabilistic principal component analysis (MPPCA) models are suitable to process with any probability density function. But, it suffers a drawback that the needed charts are too many to be watched in practice while the number of sub-models in MPPCA is large. Different from existing MPPCA, this paper proposes a novel method, which integrates every monitoring chart of MPPCA models into only one chart via probability and field process monitoring can rely on just one chart. The application in real chemical separation process shows validity of the proposed method.

Keywords

Gaussian Mixture Model Control Limit Monitoring Method Monitoring Index Gaussian Density Function 
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

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

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