• DocumentCode
    1737422
  • Title

    A new method of plotting and navigating self organising maps for improved condition monitoring and prognosis

  • Author

    Burton, Bruce ; Harley, Ronald G.

  • Author_Institution
    Sch. of Electr. & Electron. Eng., Natal Univ., Durban, South Africa
  • Volume
    2
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    980
  • Abstract
    This paper proposes a powerful new method for plotting self organising maps which enables more effective convergence analysis for training algorithms, and facilitates clear identification and distinction of major and minor class or feature boundaries. A simple example is presented to show how these boundaries enable direct interactive and automatic classification, without an output classification layer, and facilitate adaptive trajectory analysis for temporal condition monitoring with inherent time to failure prediction capabilities
  • Keywords
    classification; condition monitoring; failure analysis; identification; learning (artificial intelligence); reliability theory; self-organising feature maps; adaptive trajectory analysis; class boundaries; condition monitoring; condition prognosis; convergence analysis; distinction; feature boundaries; identification; navigating; plotting; self organising maps; temporal condition monitoring; time to failure prediction; training algorithms; Algorithm design and analysis; Clustering algorithms; Condition monitoring; Failure analysis; Navigation; Neural networks; Neurons; Power engineering and energy; Training data; Trajectory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industry Applications Conference, 2000. Conference Record of the 2000 IEEE
  • Conference_Location
    Rome
  • ISSN
    0197-2618
  • Print_ISBN
    0-7803-6401-5
  • Type

    conf

  • DOI
    10.1109/IAS.2000.881951
  • Filename
    881951