• DocumentCode
    1240275
  • Title

    Dynamic process monitoring and fault diagnosis with qualitative models

  • Author

    Vinson, Jonathan M. ; Ungar, Lyle H.

  • Author_Institution
    Dept. of Chem. Eng., Pennsylvania Univ., Philadelphia, PA, USA
  • Volume
    25
  • Issue
    1
  • fYear
    1995
  • fDate
    1/1/1995 12:00:00 AM
  • Firstpage
    181
  • Lastpage
    189
  • Abstract
    Qualitative Modeling and Interpretation (QMI) and Qmimic are online monitoring and diagnosis systems which use multiple qualitative models of a plant to monitor noisy data streams and rapidly diagnose faults from observed dynamic behavior. Both systems continue monitoring after faults have occurred. QMI simulates normal and faulty plant behavior off-line using purely qualitative QSIM models, and uses plant data to select the correct model, yielding a diagnosis. Qmimic incrementally simulates online qualitative models which describe the current behavior of the plant, using plant data to constrain further predictions and select between the models. Although both systems are based on qualitative models of the plant, Qmimic also incorporates semi-quantitative data (quantitative ranges and bounding envelopes) into the qualitative simulation in order to achieve better predictions. QMI and Qmimic are described and compared in detail, and both are tested on a simulated chemical reactor
  • Keywords
    computerised monitoring; digital simulation; factory automation; fault diagnosis; monitoring; QMI; Qmimic; Qualitative Modeling and Interpretation; dynamic process monitoring; multiple qualitative models; noisy data streams; online diagnosis; online monitoring; qualitative QSIM models; rapid fault diagnosis; semi-quantitative data; simulation; Acoustic noise; Acoustic sensors; Chemical reactors; Diagnostic expert systems; Fault diagnosis; Knowledge based systems; Monitoring; Predictive models; Sensor systems; Testing;
  • fLanguage
    English
  • Journal_Title
    Systems, Man and Cybernetics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9472
  • Type

    jour

  • DOI
    10.1109/21.362954
  • Filename
    362954