Information Theory-based Approaches for Causality Analysis with Industrial Applications

• Author / Creator

  Duan, Ping

• 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.

• Subjects / Keywords

  • Transfer entropy
  • Information theory
  • Causality analysis
  • Root cause analysis

• Graduation date

  Spring 2014

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R38D74

• 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.