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

  • Condition-Based Maintenance
  • Mulistate Degradation
  • Reliability
  • Condition Monitoirng

• Graduation date

  Fall 2013

• 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.