Model Based Fault Detection and Diagnosis of LTI Systems using Likelihood Ratio Tests

• Author / Creator

  Kiasi, Fariborz

• This study aims to develop a new framework to detect, isolate and estimate the magnitude of additive faults that occur in linear time invariant (LTI) systems. This study starts with introduction of a new framework to deal with additive step type faults to improve the shortcomings attributed to detection of time of occurrence of the fault (TOF) in existing methods. In the next step, a Bayesian approach is used to decouple detection and isolation phases from the estimation of fault magnitude. The final part of this study is dedicated to investigation of more realistic ramp and truncated ramp type faults.

• Subjects / Keywords

  • Fault Detection and Isolation
  • Generalized Likelihood Ratio Test
  • Kalman Filter
  • Fault Detection and Diagnosis

• Graduation date

  Spring 2014

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3F47H28W

• 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.