• DocumentCode
    2314453
  • Title

    Machinery Condition Monitoring System Selection A Multi-Objective Decision Approach Using GA

  • Author

    Verma, A.K. ; Srividya, A. ; Ramesh, P.G.

  • Author_Institution
    Indian Inst. of Technol., Mumbai
  • fYear
    2008
  • fDate
    16-18 July 2008
  • Firstpage
    787
  • Lastpage
    792
  • Abstract
    In this paper, a framework for Condition Based Maintenance (CBM) of large engineering systems has been suggested to address the need to optimize the characteristics of both the condition monitoring system and the plant that is being maintained at a macro level. CBM for large engineering systems has been modeled using Markov process. The issues which are critical to CBM, namely, predictability, detectability as well as implications of availability and cost have been considered in the framework to provide effective decision support. The application of the framework has been demonstrated using a numerical example. The trade offs between the Pareto optimal solutions of various objectives have been studied and the effects of variation of the maintenance objective function values with detectability and predictability have been brought out.
  • Keywords
    Markov processes; Pareto analysis; condition monitoring; decision theory; genetic algorithms; maintenance engineering; GA; Markov process; Pareto optimal solutions; large engineering systems; machinery condition monitoring system; multiobjective decision approach; Aerospace engineering; Condition monitoring; Costs; Inspection; Machinery; Maintenance engineering; Markov processes; Power engineering and energy; Reliability engineering; Systems engineering and theory; Condition Based Maintenance; Genetic Algorithms; Multi objective optimisation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Emerging Trends in Engineering and Technology, 2008. ICETET '08. First International Conference on
  • Conference_Location
    Nagpur, Maharashtra
  • Print_ISBN
    978-0-7695-3267-7
  • Electronic_ISBN
    978-0-7695-3267-7
  • Type

    conf

  • DOI
    10.1109/ICETET.2008.127
  • Filename
    4580008