Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2012 | 22 | 1 | 41-53

Tytuł artykułu

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

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
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.

Rocznik

Tom

22

Numer

1

Strony

41-53

Opis fizyczny

Daty

wydano
2012
otrzymano
2011-01-18
poprawiono
2011-07-20

Twórcy

autor
  • Department of Chemical and Materials Engineering, University of Alberta, 7th Floor, ECERF, 9107 116 Street, Edmonton, AB T6G 2G6, Canada
  • Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, 1 Tsinghuayuan, Haidian District, Beijing 100084, China
  • Department of Chemical and Materials Engineering, University of Alberta, 7th Floor, ECERF, 9107 116 Street, Edmonton, AB T6G 2G6, Canada
autor
  • Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, 1 Tsinghuayuan, Haidian District, Beijing 100084, China

Bibliografia

  • Alonso, C.J., Llamas, C., Maestro, J.A. and Pulido, B. (2003). Diagnosis of dynamic systems: A knowledge model that allows tracking the system during the diagnosis process, in P.W.H. Chung, C. Hinde and M. Ali (Eds.), Developments in Applied Artificial Intelligence, Lecture Notes in Artificial Intelligence, Vol. 2718, Springer, Berlin, pp. 208-218.
  • Bauer, M., Cox, J.W., Caveness, M.H., Downs, J.J. and Thornhill, N.F. (2007). Finding the direction of disturbance propagation in a chemical process using transfer entropy, IEEE Transactions on Control Systems Technology 15(1): 12-21.
  • Bauer, M. and Thornhill, N.F. (2008). A practical method for identifying the propagation path of plant-wide disturbances, Journal of Process Control 18(7-8): 707-719.
  • Cheng, H., Tikkala, V.-M., Zakharov, A., Myller, T. and JamsaJounela, S.L. (2011). Application of the enhanced dynamic causal digraph method on a three-layer board machine, IEEE Transactions on Control Systems Technology 19(3): 644-655.
  • Fagarasan, I., Ploix, S. and Gentil, S. (2004). Causal fault detection and isolation based on a set-membership approach, Automatica 40(12): 2099-2110.
  • Fedai, M. and Drath, R. (2005). CAEX-A neutral data exchange format for engineering data, Automation Technology in Practice-ATP International 1(3): 43-51.
  • Gigi, S. and Tangirala, A.K. (2010). Quantitative analysis of directional strengths in jointly stationary linear multivariate processes, Biological Cybernetics 103(2): 119-133.
  • Górny, B. (2001). Consistency-Based Reasoning in ModelBased Diagnosis, Ph.D. thesis, AGH University of Science and Technology, Cracow.
  • Iri, M., Aoki, K., O'shima, E. and Matsuyama, H. (1979). An algorithm for diagnosis of system failures in the chemical process, Computers and Chemical Engineering 3(1-4): 489-493.
  • Jan, A., Jonas, B., Erik, F., Krysander, M. and Lars, N. (2007). Safety analysis of autonomous systems by extended fault tree analysis, International Journal of Adaptive Control and Signal Processing 21(2-3): 287-298.
  • Ligęza, A. (1996). A note on systematic conflict generation in CA-EN-type causal structures, LAAS Report No. 96317, Laboratory for Analysis and Architecture of Systems, Toulouse.
  • Ligęza, A. and Kościelny, J.M. (2008). A new approach to multiple fault diagnosis: A combination of diagnostic matrices, graphs, algebraic and rule-based models. The case of two-layer models, International Journal of Applied Mathematics and Computer Science 18(4): 465-476, DOI: 10.2478/v10006-008-0041-8.
  • Korbicz, J. and Kościelny, J.M. (Eds.) (2010). Modeling, Diagnostics and Process Control: Implementation in the DiaSter System, Springer-Verlag, Berlin/Heidelberg.
  • Leyval, L., Gentil, S. and Feray-Beaumont, S. (1994). Modelbased causal reasoning for process supervision, Automatica 30(8): 1295-1306.
  • Li, Q. and Racine, J.S. (2007). Nonparametric Econometrics: Theory and Practice, Princeton University Press, Princeton, NJ.
  • Lungarella, M., Ishiguro, K., Kuniyoshi, Y. and Otsu, N. (2007). Methods for quantifying the causal structure of bivariate time series, International Journal of Bifurcation and Chaos 17(3): 903-921.
  • Maurya, M.R., Rengaswamy, R. and Venkatasubramanian, V. (2003a). A systematic framework for the development and analysis of signed digraphs for chemical processes: 1. Algorithms and analysis, Industrial and Engineering Chemistry Research 42(20): 4789-4810.
  • Maurya, M.R., Rengaswamy, R. and Venkatasubramanian, V. (2003b). A systematic framework for the development and analysis of signed digraphs for chemical processes: 2. Control loops and flowsheet analysis, Industrial and Engineering Chemistry Research 42(20): 4811-4827.
  • Maurya, M.R., Rengaswamy, R. and Venkatasubramanian, V. (2004). Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets, Engineering Applications of Artificial Intelligence 17(5): 501-518.
  • Montmain, J. and Gentil, S. (2000). Dynamic causal model diagnostic reasoning for online technical process supervision, Automatica 36(8): 1137-1152.
  • Mosterman, P.J. and Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions, IEEE Transactions on Systems, Man, and Cybernetics: Part A 29(6): 554-565.
  • Oyeleye, O.O. and Kramer, M.A. (1988). Qualitative simulation of chemical process systems: Steady-state analysis, AIChE Journal 34(9): 1441-1454.
  • Palmer, C. and Chung, P.W.H. (1999). Verifying signed directed graph models for process plants, Computers & Chemical Engineering 23(Suppl. 1): S394-S391.
  • Palmer, C. and Chung, P.W.H. (2000). Creating signed directed graph models for process plants, Industrial and Engineering Chemistry Research 39(20): 2548-2558.
  • Pastor, J., Lafon, M., Trave-Massuyes, L., Demonet, J.-F., Doyon, B. and Celsis, P. (2000). Information processing in large-scale cerebral networks: The causal connectivity approach, Biological Cybernetics 82(1): 49-59.
  • Schreiber, T. (2000). Measuring information transfer, Physical Review Letters 85(2): 461-464.
  • Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis, Chapman and Hall, London/New York, NY.
  • Staroswiecki, M. (2000). Quantitative and qualitative models for fault detection and isolation, Mechanical Systems and Signal Processing 14(3): 301-325.
  • Tangirala, A.K., Shah, S.L. and Thornhill, N.F. (2005). PSCMAP: A new tool for plant-wide oscillation detection, Journal of Process Control 15(8): 931-941.
  • Thambirajah, J., Benabbas, L., Bauer, M. and Thornhill, N.F. (2009). Cause-and-effect analysis in chemical processes utilizing XML: Plant connectivity and quantitative process history, Computers and Chemical Engineering 33(2): 503-512.
  • Yang, F., Shah, S.L. and Xiao, D. (2010a). SDG (Signed Directed Graph) based process description and fault propagation analysis for a tailings pumping process, Proceedings of the 13th IFAC Symposium on Automation in Mining, Mineral and Metal Processing, Cape Town, South Africa, pp. 50-55.
  • Yang, F., Xiao, D. and Shah, S.L. (2010b). Qualitative fault detection and hazard analysis based on signed directed graphs for large-scale complex systems, in W. Zhang (Ed.), Fault Detection, IN-TECH, Vukovar, pp. 15-50.
  • Yim, S.Y., Ananthakumar, H.G., Benabbas, L., Horch, A., Drath, R. and Thornhill, N.F. (2006). Using process topology in plant-wide control loop performance assessment, Computers and Chemical Engineering 31(2): 86-99.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.bwnjournal-article-amcv22i1p41bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.