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Developing MATLAB Tools for Data Based Alarm Management and Causality Analysis Open Access


Other title
MATLAB tools
Causality analysis
Alarm Management
Type of item
Degree grantor
University of Alberta
Author or creator
Amin, Md Shahedul
Supervisor and department
Shah, Sirish (Chemical and Materials Engineering)
Chen, Tongwen (Electrical and Computer Engineering)
Examining committee member and department
Ardakani, Masoud (Electrical and Computer Engineering)
Lynch, Alan (Electrical and Computer Engineering)
Department of Electrical and Computer Engineering
Date accepted
Graduation date
Master of Science
Degree level
In this thesis, two Graphical User Interfaces (GUIs) are designed in MATLAB to perform causality analysis and alarm management. Finding out the root-cause of a fault scenario or an abnormality in a large industrial process typically requires one to logically analyze cause and effect relationships between variables. Causality analysis can play a vital role to capture process connectivity and topology and to identify relationships among the variables in a process. The availability of large volumes of industrial process data has now opened the way to develop data-driven methods for causality detection. In the first tool, different techniques of data visualization along with three data-driven methods of causality analysis, namely, cross-correlation, transfer entropy, and Granger causality, have been implemented. Case studies are provided to illustrate the capture of process connectivity using both transfer entropy and Granger causality methods. Recent studies have shown that the number of alarms in process industries is far more than the approved standards because of a very high number of false and nuisance alarms. The large number of alarms distracts the operator from safe and regulatory monitoring of the processes, which leads to plantwide upset and affects overall productivity of the system. Therefore root cause identification of faults and alarm management have become very important for process industries. The second tool for alarm management is proposed where historic alarm data can be used to find out the top bad actors in the system. Also functions for correlated alarms and similarity between different alarm flood analysis have been implemented in the GUI for easier root cause identification.
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.
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