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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Martin, E.B., Morris, A.J.: Non-parametric Confidence Bounds for Process Performance Monitoring Charts. Journal of Process Control 6(6), 349–358 (1996)CrossRefGoogle Scholar
  2. 2.
    Chen, J.H., Liu, J.L.: Mixture Principal Component Analysis Models for Process Monitoring. Ind. Eng. Chem. Res. 38, 1478–1488 (1999)CrossRefGoogle Scholar
  3. 3.
    Zhang, F.: A Mixture Probabilistic PCA Model for Multivariate Processes Monitoring. In: Proceeding of the 2004 American Control Conference, Boston, pp. 3111–3115 (2004)Google Scholar
  4. 4.
    Michael, E.T., Christopher, M.B.: Mixtures of Principal Component Analyzers. Neural Computation 11(2), 443–482 (1999)CrossRefGoogle Scholar
  5. 5.
    Choi, S.W., Park, J.H., Lee, I.B.: Process Monitoring Using a Gaussian Mixture Model via Principal Component Analysis and Discriminant Analysis. Computers and Chemical Engineering 28(8), 1377–1387 (2004)Google Scholar
  6. 6.
    Kim, D.S., Lee, I.B.: Process Monitoring Based on Probabilistic PCA. Chemometrics and Intelligent Laboratory Systems 67(2), 109–123 (2003)CrossRefGoogle Scholar
  7. 7.
    Mario, A.T.F., Anil, K.J.: Unsupervised Learning of Finite Mixture Models. IEEE Trans. on P.A.M.I. 24(3), 381–396 (2002)Google Scholar
  8. 8.
    Liu, F., Zhao, Z.G.: Chemical Separation Process Monitoring Based on Nonlinear Principal Component Analysis. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 798–803. Springer, Heidelberg (2004)CrossRefGoogle Scholar

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

Personalised recommendations