• DocumentCode
    1153835
  • Title

    Analysis of extended partial least squares for monitoring large-scale processes

  • Author

    Chen, Qian ; Kruger, Uwe

  • Author_Institution
    Coll. of Aerosp. Eng., Nanjing Univ. of Aeronaut. & Astronaut., China
  • Volume
    13
  • Issue
    5
  • fYear
    2005
  • Firstpage
    807
  • Lastpage
    813
  • Abstract
    This brief analyzes the recently proposed extended partial least squares (EPLS) algorithm and reveals that it does not: 1) allow the generalized score variables to be geometrically interpreted, 2) reconstruct the recorded process variables, and 3) produce statistically independent variables for process monitoring. To overcome these deficiencies, an improved EPLS algorithm is introduced, which utilizes generalized scores to identify statistical monitoring models. The brief finally presents an industrial application study of a chemical reaction process to show that improved EPLS offers enhanced diagnosis of abnormal process behavior.
  • Keywords
    chemical industry; large-scale systems; least mean squares methods; process monitoring; statistical analysis; chemical reaction process; extended partial least squares; large-scale processes; process monitoring; statistical monitoring models; Algorithm design and analysis; Chemical industry; Chemical processes; Fault diagnosis; Large-scale systems; Least squares methods; Monitoring; Page description languages; Process control; Statistics; Data compression; fault diagnosis; process control; process monitoring; statistics;
  • fLanguage
    English
  • Journal_Title
    Control Systems Technology, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1063-6536
  • Type

    jour

  • DOI
    10.1109/TCST.2005.852113
  • Filename
    1501865