• DocumentCode
    2887397
  • Title

    Structural health monitoring at Los Alamos National Laboratory

  • Author

    Farrar, Charles R. ; Doebling, Scott W.

  • Author_Institution
    Los Alamos Nat. Lab., NM, USA
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    42401
  • Lastpage
    42404
  • Abstract
    Los Alamos National Laboratory (LANL) has several ongoing programs to identify damage in structures and mechanical systems from changes in their dynamic characteristics. This paper provides a summary of LANL´s involvement with this technology, past experiences in this field including damage detection studies on large civil engineering infrastructure and the directions that research in this area will be taking in the future. The research began by taking a strictly model-based approach to the vibration-based damage detection problem. Recent work has recognized that it is more appropriate to view the damage detection problem as an exercise in statistical pattern recognition. Therefore, a general statistical pattern recognition paradigm will be proposed
  • Keywords
    condition monitoring; bridges; complex structures; damage identification; dynamic characteristics change; dynamic response; eight DOF test system; large civil engineering infrastructure; machine learning; mechanical systems; modal tests; model-based approach; rotating machinery monitoring; seismically-induced buckling; statistical pattern recognition; structural health monitoring; supervised learning; unsupervised learning; vibration-based damage detection;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Condition Monitoring: Machinery, External Structures and Health (Ref. No. 1999/034), IEE Colloquium on
  • Conference_Location
    Birmingham
  • Type

    conf

  • DOI
    10.1049/ic:19990185
  • Filename
    772130