Condition Based Maintenance Decision Making with Delay Time Modeling

  • Author / Creator
    Lipi, Tahmina Ferdousi
  • This research targets to develop condition based maintenance models for better maintenance decisions. We address a condition and age based replacement decision problem using the complete history of measured condition observations to minimize long-run average cost or maximize long-run average availability, or both. To estimate the residual lifetime distribution conditional on the history of observed condition information and current age, a delay time model (DTM) based stochastic filtering process (SFP) is used. A long-run average cost model and a long-run average availability model are analyzed in order to develop the theorems necessary for determining the optimum replacement time. A multi-objective decision frontier is proposed that will help maintenance managers deal with trade-offs between the two objectives to minimize the cost and to maximize availability simultaneously. We also proposed models to integrate imperfect maintenance while making replacement decisions. Finally, in order to show the effectiveness of our proposed models, numerical examples are presented.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Mechanical Engineering
  • Specialization
    • Engineering Mangement
  • Supervisor / co-supervisor and their department(s)
    • Dr. Ming J. Zuo, Dept. of Mechanical Engineering
  • Examining committee members and their departments
    • Dr. Stan Karapetrovic, Dept. of Mechanical Engineering
    • Dr. Mike Lipsett, Dept. of Mechanical Engineering