Total projection to latent structures for process monitoring

Authors

  • Donghua Zhou,

    1. Dept. of Automation, TNList, Tsinghua University, Beijing 100084, P.R. China
    Search for more papers by this author
  • Gang Li,

    Corresponding author
    1. Dept. of Automation, TNList, Tsinghua University, Beijing 100084, P.R. China
    • Dept. of Automation, TNList, Tsinghua University, Beijing 100084, P.R. China
    Search for more papers by this author
  • S. Joe Qin

    Corresponding author
    1. The Mork Family Department of Chemical Engineering and Materials Science, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089
    • The Mork Family Department of Chemical Engineering and Materials Science, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089
    Search for more papers by this author

Abstract

Partial least squares or projection to latent structures (PLS) has been used in multivariate statistical process monitoring similar to principal component analysis. Standard PLS often requires many components or latent variables (LVs), which contain variations orthogonal to Y and useless for predicting Y. Further, the X-residual of PLS usually has quite large variations, thus is not proper to monitor with the Q-statistic. To reduce false alarm and missing alarm rates of faults related to Y, a total projection to latent structures (T-PLS) algorithm is proposed in this article. The new structure divides the X-space into four parts instead of two parts in standard PLS. The properties of T-PLS are studied in detail, including its relationship to the orthogonal PLS. Further study shows the space decomposition on X-space induced by T-PLS. Fault detection policy is developed based on the T-PLS. Case studies on two simulation examples show the effectiveness of the T-PLS based fault detection methods. © 2009 American Institute of Chemical Engineers AIChE J, 2010

Ancillary