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Fault Detection and Isolation Based on Hidden Markov Models Open Access

Descriptions

Other title
Subject/Keyword
Time Varying
Robustness
Fault Detection
Hidden Markov Models
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Sammaknejad, Nima
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Examining committee member and department
Yeung, Tony (Chemical and Materials Engineering)
Dubljevic, Stevan (Chemical and Materials Engineering)
Mhaskar, Prashant (Chemical Engineering)
Afacan, Artin (Chemical and Materials Engineering)
Barczyk, Martin (Mechanical Engineering)
Department
Department of Chemical and Materials Engineering
Specialization
Process Control
Date accepted
2015-09-29T17:37:20Z
Graduation date
2015-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
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.
Language
English
DOI
doi:10.7939/R3CJ87X8P
Rights
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. 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
N. Sammaknejad, B. Huang, W. Xiong, A. Fatehi, F. Xu, and A. Espejo (2015). "Operating Condition Diagnosis Based on HMM with Adaptive Transition Probabilities in the Presence of Missing Observations". AIChE Journal, 61(2), 477-492.N. Sammaknejad, B. Huang, and Y. Lu (2015). "Robust Diagnosis of Operating Mode Based on Time Varying Hidden Markov Models". IEEE Transactions on Industrial Electronics. DOI: 10.1109/TIE.2015.2478743.N. Sammaknejad, B. Huang, A. Fatehi, Y. Miao, F. Xu, and A. Espejo (2014). "Adaptive Monitoring of the Process Operation Based on Symbolic Episode Representation and Hidden Markov Models with Application Toward an Oil Sand Primary Separation". Computers and Chemical Engineering, 71, 281-297.N. Sammaknejad, and B. Huang (2014). "Process Monitoring Based on Symbolic Episode Representation and Hidden Markov Models - A Moving Window Approach". Proceedings of the 5th International Symposium on Advanced Control of Industrial Processes (ADCONIP). Hiroshima, Japan.N. Sammaknejad, B. Huang, R. S. Sanders, Y. Miao, F. Xu, A. Espejo (2015). "Adaptive Soft Sensing and On-line Estimation of the Critical Minimum Velocity with Application to an Oil Sand Primary Separation Vessel". Proceedings of the IFAC 9th International Symposium on Advanced Control of Chemical Processes (ADCHEM). Whistler, Canada.

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