• DocumentCode
    658065
  • Title

    Model-based process diagnosis: Bond Graph and Signed Directed Graph tools

  • Author

    Smaili, Rahma ; El Harabi, Rafika ; Abdelkrim, Mohamed Naceur

  • Author_Institution
    Nat. Eng. Sch. of Gabes, Univ. of Gabes, Elkhatab, Tunisia
  • fYear
    2013
  • fDate
    6-8 May 2013
  • Firstpage
    782
  • Lastpage
    787
  • Abstract
    This paper deals with a fault detection and isolation for linear systems using causal graphical approaches. The monitoring based on qualitative models (Signed Directed Graph) enhance fault localization by analyzing initial and final responses of system variables due to deviations, these dynamic behaviors are deduced using causal paths and propagations from the fault node to system variable node. However, the quantitative models (Bond Graph), as a unified multidisciplinary tool, is adopted to detect and isolate faults by generating analytical redundancy relations (ARRs) through covering causal paths from unknown variables to known variables. The interest and efficiency of the proposed methods are illustrated via a numerical example.
  • Keywords
    bond graphs; causality; directed graphs; fault location; linear systems; ARR; analytical redundancy relation; bond graph; causal graphical approach; causal path; dynamic behavior; fault detection; fault isolation; fault localization; fault node; linear system; model-based process diagnosis; qualitative model; signed directed graph tools; system variable node; system variable response; Analytical models; Educational institutions; Equations; Fault diagnosis; Hydraulic systems; Mathematical model; Prediction algorithms; Bond Graph; Fault Diagnosis; Hydraulic system; Signed Directed Graph; Structural Analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Decision and Information Technologies (CoDIT), 2013 International Conference on
  • Conference_Location
    Hammamet
  • Print_ISBN
    978-1-4673-5547-6
  • Type

    conf

  • DOI
    10.1109/CoDIT.2013.6689642
  • Filename
    6689642