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


Other title
Fault Detection and Isolation
Fault Detection and Diagnosis
Generalized Likelihood Ratio Test
Kalman Filter
Type of item
Degree grantor
University of Alberta
Author or creator
Kiasi, Fariborz
Supervisor and department
Shah, Sirish (Chemical and Materials Engineering)
Examining committee member and department
Liu, Jinfeng (Chemical and Materials Engineering)
Shah, Sirish (Chemical and Materials Engineering)
Huang, Biao (Chemical and Materials Engineering)
Chen, Tongwen (Electrical and Computer Engineering)
Narasimhan, Shankar (Chemical Engineering, IIT Madras, India)
Department of Chemical and Materials Engineering
Process Control
Date accepted
Graduation date
Doctor of Philosophy
Degree level
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.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
F. Kiasi, J. Prakash, S. L. Shah, “An alternative approach to implementation of the generalized likelihood ratio test for fault detection and isolation”, Industrial & Engineering Chemistry Research 52 (35) (2013) 12482-12489.F. Kiasi, J. Prakash, S. Patwardhan, S. Shah, “A unified framework for fault detection and isolation of sensor and actuator biases in linear time invariant systems using marginalized likelihood ratio test with uniform priors”, Journal of Process Control 23 (9) (2013) 1350-1361.F. Kiasi, J. Prakash, S. L. Shah, “Fault detection using marginalized likelihood ratio and uniform priors: Justifications and challenges”, IEEE CDC 2012, Maui, HI, USA.F. Kiasi, J. Prakash, S. L. Shah, “A Novel Approach to Fault Detection and Isolation of Linear Systems Using the Modified Marginalized Likelihood Ratio Test and Uniform Prior”, 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, August 29-31, 2012, National Autonomous University of Mexico, Mexico City, Mexico.F. Kiasi, J. Prakash, S. L. Shah, “Fault Detection and Isolation of Benchmark Wind Turbine Using the Likelihood Ratio Test”, IFAC 2011 World Congress, Università Cattolica del Sacro Cuore, Milano, Italy.F. Kiasi, J. Prakash, S. L. Shah, “Model Based Fault Tolerant Control Using the Marginalized Likelihood Ratio Test”, IEEE control and fault tolerant control systems, SysTol 2010, Nice, France.

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File title: Fault Signature Matrices of an Additive Ramp Type Fault
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