Fault Detection and Isolation Based on Hidden Markov Models

• Author / Creator

  Sammaknejad, Nima

• A large volume of literature exists on fault detection and isolation for industrial processes. In a general view, these various methods may be divided into process model based and process history based fault diagnosis. In both classes, there has been a recent focus on extracting the temporal information corresponding to process transitions between various operating modes. In this context, Hidden Markov Models (HMMs) have been introduced and applied for process monitoring and diagnosis purposes.
  The main objective of this thesis is to develop novel HMM based approaches
  to diagnose various operating modes of a process. Mode in this thesis refers to process operational status such as normal operating condition or fault.

• Subjects / Keywords

  • Hidden Markov Models
  • Time Varying
  • Robustness
  • Fault Detection

• Graduation date

  Fall 2015

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3CJ87X8P

• 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.