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Equipment Degradation Diagnostics and Prognostics Under a Multistate Deterioration Process

  • Author / Creator
    Moghaddass, Ramin
  • The increasing level of system complexity in the current competitive market implies that efficient asset management is of paramount importance, particularly for systems with costly downtime and failure. Timely detection of faults and failures through an efficient reliability and health management framework allows for appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize unnecessary maintenance actions. This thesis employs a general stochastic process - the Nonhomogeneous Continuous-Time Hidden Semi-Markov Process - to model a condition-monitored degradation process with hidden states. This thesis also proposes an unsupervised learning process, which can be used to estimate the characteristic parameters of the degradation and observation processes. It then develops dynamic diagnostic and prognostic measures for online health monitoring. Finally, it introduces a condition-based replacement policy that can be used as an online tool to determine when to replace a degraded device under condition monitoring.

  • Subjects / Keywords
  • Graduation date
    2013-11
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3PC2TM13
  • 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
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Mechanical Engineering
  • Specialization
    • Engineering Management
  • Supervisor / co-supervisor and their department(s)
    • Ming J Zuo (Mechanical Engineeing)
  • Examining committee members and their departments
    • John Doucette (Mechanical Engineeing)
    • Faisal Khan (Process Engineeing)
    • Jie Han (Electrical and Computer Engineering)
    • Armann Ingolfsson (School of Business)