• DocumentCode
    1812442
  • Title

    Sensor placement for fault diagnosis using genetic algorithm

  • Author

    Guoyi Chi ; Danwei Wang ; Ming Yu ; Alavi, Meysam ; Tung Le ; Ming Luo

  • Author_Institution
    Centre for E-City, Nanyang Technol. Univ., Singapore, Singapore
  • fYear
    2012
  • fDate
    17-21 Sept. 2012
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    This paper presents a novel methodology for the purpose of fault detection and isolation (FDI) to a two-tank system. This new methodology benefits from the basic facts that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will cause the corresponding ARRs to change. Based on these facts, the minimal isolation set as an important concept is introduced to make each fault in the fault set F detectable and isolable. Then, the sensor placement problem consists in determining an optimal minimal isolation set associated with the least number of sensors. A dedicated genetic algorithm is developed to solve the formulated sensor placement problem. A case study of a two-tank system shows that the proposed methodology performs well.
  • Keywords
    condition monitoring; fault diagnosis; genetic algorithms; sensor placement; tanks (containers); tanning; analytical redundancy relations; fault diagnosis; genetic algorithm; optimal minimal isolation; sensor placement; two tank system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Emerging Technologies & Factory Automation (ETFA), 2012 IEEE 17th Conference on
  • Conference_Location
    Krakow
  • ISSN
    1946-0740
  • Print_ISBN
    978-1-4673-4735-8
  • Electronic_ISBN
    1946-0740
  • Type

    conf

  • DOI
    10.1109/ETFA.2012.6489615
  • Filename
    6489615