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Information Theory-based Approaches for Causality Analysis with Industrial Applications Open Access


Other title
information theory
causality analysis
transfer entropy
root cause analysis
Type of item
Degree grantor
University of Alberta
Author or creator
Duan, Ping
Supervisor and department
Chen, Tongwen (Electrical and Computer Engineering)
Shah, Sirish L. (Chemical and Materials Engineering)
Examining committee member and department
Zhao, Qing (Electrical and Computer Engineering)
Shah, Sirish L. (Chemical and Materials Engineering)
Ardakani, Masoud (Electrical and Computer Engineering)
Chen, Tongwen (Electrical and Computer Engineering)
Kwok, Ezra (UBC Chemical and Biological Engineering)
Department of Electrical and Computer Engineering
Control Systems
Date accepted
Graduation date
Doctor of Philosophy
Degree level
Detection and diagnosis of plant-wide abnormalities and disturbances are major problems in large-scale complex systems. To determine the root cause(s) of specific abnormalities, it is important to capture the process connectivity and investigate the fault propagation pathways, in which causality detection plays a significant and central role. This thesis focuses mainly on information theory-based approaches for causality analysis that are suitable for both linear and nonlinear process relationships. Previous studies have shown that the transfer entropy approach is a very useful tool in quantifying causal influence by inferring material and information pathways in a system. However, the traditional transfer entropy method only determines whether there is causality from one variable to another; it cannot tell whether the causal influence is along a direct pathway or indirect pathways through some intermediate variables. In order to detect and discriminate between direct and indirect causality relationships, a direct transfer entropy concept is proposed in this thesis. Specifically, a differential direct transfer entropy concept is defined for continuous-valued random variables, and a normalization method for the differential direct transfer entropy is presented to determine the connectivity strength of direct causality. A key assumption for the transfer entropy method is that the sampled data should follow a well-defined probability distribution; yet this assumption may not hold for all types of industrial process data. A new information theory-based distribution-free measure, transfer 0-entropy, is proposed for causality analysis based on the definitions of 0-entropy and 0-information without assuming a probability space. For the cases of more than two variables, a direct transfer 0-entropy concept is presented to detect whether there is a direct information and/or material flow pathway from one variable to another. Additionally, estimation methods for the transfer 0-entropy and the direct transfer 0-entropy are also provided. For root cause diagnosis of plant-wide oscillations, comparisons are given between the usefulness of these two information theory-based causality detection methods and other four widely used methods: the Granger causality analysis method, the spectral envelope method, the adjacency matrix method, and the Bayesian network inference method. All six methods are applied to a benchmark industrial data set and a set of guidelines and recommendations on how to deal with the root cause diagnosis problem is discussed.
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
P. Duan, F. Yang, T. Chen, and S.L. Shah. Direct causality detection via the transfer entropy approach. IEEE Transactions on Control Systems Technology, 21(6):2052–2066, 2013.P. Duan, F. Yang, T. Chen, and S.L. Shah. Detection of direct causality based on process data. In Proceedings of 2012 American Control Conference, pages 3522–3527, Montreal, Canada, 2012.P. Duan, S.L. Shah, T. Chen, and F. Yang. Methods for detection and root cause diagnosis of plant-wide oscillations. accepted by AIChE Journal, 2014.

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