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

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

[1]  Nina F. Thornhill,et al.  Using process topology in plant-wide control loop performance assessment , 2006, Comput. Chem. Eng..

[2]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[3]  Pieter J. Mosterman,et al.  Diagnosis of continuous valued systems in transient operating regions , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[4]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[5]  Qi Li,et al.  Nonparametric Econometrics: Theory and Practice , 2006 .

[6]  O. O. Oyeleye,et al.  Qualitative simulation of chemical process systems: Steady‐state analysis , 1988 .

[7]  Jean-François Démonet,et al.  Information processing in large-scale cerebral networks: the causal connectivity approach , 2000, Biological Cybernetics.

[8]  M. Staroswiecki.  QUANTITATIVE AND QUALITATIVE MODELS FOR FAULT DETECTION AND ISOLATION , 2000 .

[9]  Stéphane Ploix,et al.  Causal fault detection and isolation based on a set-membership approach , 2004, Autom..

[10]  Yasuo Kuniyoshi,et al.  Methods for Quantifying the Causal Structure of bivariate Time Series , 2007, Int. J. Bifurc. Chaos.

[11]  Jacky Montmain,et al.  Dynamic causal model diagnostic reasoning for online technical process supervision , 2000, Autom..

[12]  Erik Frisk,et al.  Safety analysis of autonomous systems by extended fault tree analysis , 2007 .

[13]  Claire Palmer,et al.  Creating signed directed graph models for process plants , 1999 .

[14]  Raghunathan Rengaswamy,et al.  Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets , 2004, Eng. Appl. Artif. Intell..

[15]  Nina F. Thornhill,et al.  Finding the Direction of Disturbance Propagation in a Chemical Process Using Transfer Entropy , 2007, IEEE Transactions on Control Systems Technology.

[16]  Fan Yang,et al.  Qualitative Fault Detection and Hazard Analysis Based on Signed Directed Graphs for Large-Scale Complex Systems , 2010 .

[17]  Nina F. Thornhill,et al.  A practical method for identifying the propagation path of plant-wide disturbances , 2008 .

[18]  Nina F. Thornhill,et al.  PSCMAP: A new tool for plant-wide oscillation detection , 2005 .

[19]  Raghunathan Rengaswamy,et al.  A systematic framework for the development and analysis of signed digraphs for chemical processes. 2. Control loops and flowsheet analysis , 2003 .

[20]  Jozef. Koscielny Jan Maciej. Jozef Korbicz Jan Maciej Kos Korbicz,et al.  Modeling, diagnostics and process control : implementation in the DiaSter system , 2010 .

[21]  Carlos J. Alonso,et al.  Diagnosis of Dynamic Systems: A Knowledge Model That Allows Tracking the System during the Diagnosis Process , 2003, IEA/AIE.

[22]  Vesa-Matti Tikkala,et al.  Application of the enhanced dynamic causal digraph method on a three-layer board machine , 2009, 2009 European Control Conference (ECC).

[23]  M. Iri,et al.  An algorithm for diagnosis of system failures in the chemical process , 1979 .

[24]  Arun K. Tangirala,et al.  Quantitative analysis of directional strengths in jointly stationary linear multivariate processes , 2010, Biological Cybernetics.

[25]  Antoni Ligeza,et al.  A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer Models , 2008, Int. J. Appl. Math. Comput. Sci..

[26]  Sylviane Gentil,et al.  Model-based causal reasoning for process supervision , 1994, Autom..

[27]  Sirish L. Shah,et al.  SDG (Signed Directed Graph) Based Process Description and Fault Propagation Analysis for a Tailings Pumping Process , 2010 .

[28]  Nina F. Thornhill,et al.  Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history , 2009, Comput. Chem. Eng..

[29]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[30]  Paul W. H. Chung,et al.  Creating signed directed graph models for process plants , 1999 .

[31]  Antoni Ligeza.  A Note on Systematic Conflict Generation in CA-EN-type Causal Structures , 2014, ArXiv.

[32]  R. Rengaswamy,et al.  A Systematic Framework for the Development and Analysis of Signed Digraphs for Chemical Processes. 1. Algorithms and Analysis , 2003 .