Signed directed graph based modeling and its validation from process knowledge and process data

Fan Yang; Sirish L. Shah; Deyun Xiao

International Journal of Applied Mathematics and Computer Science (2012)

  • Volume: 22, Issue: 1, page 41-53
  • ISSN: 1641-876X

Abstract

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This paper is concerned with the fusion of information from process data and process connectivity and its subsequent use in fault diagnosis and process hazard assessment. The Signed Directed Graph (SDG), as a graphical model for capturing process topology and connectivity to show the causal relationships between process variables by material and information paths, has been widely used in root cause and hazard propagation analysis. An SDG is usually built based on process knowledge as described by piping and instrumentation diagrams. This is a complex and experience-dependent task, and therefore the resulting SDG should be validated by process data before being used for analysis. This paper introduces two validation methods. One is based on cross-correlation analysis of process data with assumed time delays, while the other is based on transfer entropy, where the correlation coefficient between two variables or the information transfer from one variable to another can be computed to validate the corresponding paths in SDGs. In addition to this, the relationship captured by data-based methods should also be validated by process knowledge to confirm its causality. This knowledge can be realized by checking the reachability or the influence of one variable on another based on the corresponding SDG which is the basis of causality. A case study of an industrial process is presented to illustrate the application of the proposed methods.

How to cite

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Fan Yang, Sirish L. Shah, and Deyun Xiao. "Signed directed graph based modeling and its validation from process knowledge and process data." International Journal of Applied Mathematics and Computer Science 22.1 (2012): 41-53. <http://eudml.org/doc/208099>.

@article{FanYang2012,
abstract = {This paper is concerned with the fusion of information from process data and process connectivity and its subsequent use in fault diagnosis and process hazard assessment. The Signed Directed Graph (SDG), as a graphical model for capturing process topology and connectivity to show the causal relationships between process variables by material and information paths, has been widely used in root cause and hazard propagation analysis. An SDG is usually built based on process knowledge as described by piping and instrumentation diagrams. This is a complex and experience-dependent task, and therefore the resulting SDG should be validated by process data before being used for analysis. This paper introduces two validation methods. One is based on cross-correlation analysis of process data with assumed time delays, while the other is based on transfer entropy, where the correlation coefficient between two variables or the information transfer from one variable to another can be computed to validate the corresponding paths in SDGs. In addition to this, the relationship captured by data-based methods should also be validated by process knowledge to confirm its causality. This knowledge can be realized by checking the reachability or the influence of one variable on another based on the corresponding SDG which is the basis of causality. A case study of an industrial process is presented to illustrate the application of the proposed methods.},
author = {Fan Yang, Sirish L. Shah, Deyun Xiao},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {signed directed graph; transfer entropy; process topology; fault diagnosis; process hazard assessment},
language = {eng},
number = {1},
pages = {41-53},
title = {Signed directed graph based modeling and its validation from process knowledge and process data},
url = {http://eudml.org/doc/208099},
volume = {22},
year = {2012},
}

TY - JOUR
AU - Fan Yang
AU - Sirish L. Shah
AU - Deyun Xiao
TI - Signed directed graph based modeling and its validation from process knowledge and process data
JO - International Journal of Applied Mathematics and Computer Science
PY - 2012
VL - 22
IS - 1
SP - 41
EP - 53
AB - This paper is concerned with the fusion of information from process data and process connectivity and its subsequent use in fault diagnosis and process hazard assessment. The Signed Directed Graph (SDG), as a graphical model for capturing process topology and connectivity to show the causal relationships between process variables by material and information paths, has been widely used in root cause and hazard propagation analysis. An SDG is usually built based on process knowledge as described by piping and instrumentation diagrams. This is a complex and experience-dependent task, and therefore the resulting SDG should be validated by process data before being used for analysis. This paper introduces two validation methods. One is based on cross-correlation analysis of process data with assumed time delays, while the other is based on transfer entropy, where the correlation coefficient between two variables or the information transfer from one variable to another can be computed to validate the corresponding paths in SDGs. In addition to this, the relationship captured by data-based methods should also be validated by process knowledge to confirm its causality. This knowledge can be realized by checking the reachability or the influence of one variable on another based on the corresponding SDG which is the basis of causality. A case study of an industrial process is presented to illustrate the application of the proposed methods.
LA - eng
KW - signed directed graph; transfer entropy; process topology; fault diagnosis; process hazard assessment
UR - http://eudml.org/doc/208099
ER -

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