• DocumentCode
    3285906
  • Title

    TRICODA - Complex Data Analysis and Condition Monitoring based onv Neural Network Model

  • Author

    Howells, Gareth ; Howlett, Bob ; McDonald-Maier, Klaus

  • Author_Institution
    Univ. of Kent, Canterbury
  • fYear
    2007
  • fDate
    5-8 Aug. 2007
  • Firstpage
    647
  • Lastpage
    651
  • Abstract
    The increasing availability of advanced computer equipment and sensory systems often results in large volumes of data, with subsequent difficulties in efficient analysis and real-time processing. The Tricoda initiative focuses on tools and techniques to aid in the automated analysis of large, complex systems and the data sets they generate. A novel general-purpose modelling system is employed based on the combination of a number of artificial intelligence based and conventional techniques, all integrated with a novel formal framework based on Constructive Type Theory. The tool is evaluated for the solution of a data analysis and condition monitoring case study focusing on an automotive application, specifically the automotive sector for engine control.
  • Keywords
    automotive engineering; condition monitoring; data analysis; internal combustion engines; neural nets; type theory; Tricoda; advanced computer equipment; artificial intelligence; automotive application; condition monitoring; constructive type theory; data analysis; engine control; neural network; real-time processing; sensory systems; Artificial intelligence; Automotive engineering; Availability; Condition monitoring; Data analysis; Industrial plants; Machinery; Neural networks; Sensor phenomena and characterization; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Adaptive Hardware and Systems, 2007. AHS 2007. Second NASA/ESA Conference on
  • Conference_Location
    Edinburgh
  • Print_ISBN
    978-0-7695-2866-3
  • Type

    conf

  • DOI
    10.1109/AHS.2007.107
  • Filename
    4291980