Table of Contents Author Guidelines Submit a Manuscript
Journal of Control Science and Engineering
Volume 2012, Article ID 478373, 10 pages
http://dx.doi.org/10.1155/2012/478373
Review Article

Progress in Root Cause and Fault Propagation Analysis of Large-Scale Industrial Processes

1Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China
2Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada T6G 2V4

Received 15 September 2011; Revised 17 January 2012; Accepted 1 February 2012

Academic Editor: Onur Toker

Copyright © 2012 Fan Yang and Deyun Xiao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Pearl, Causality: Models, Reasoning, and Inference, Cambridge University Press, Cambridge, UK, 2000.
  2. S. Y. Yim, H. G. Ananthakumar, L. Benabbas, A. Horch, R. Drath, and N. F. Thornhill, “Using process topology in plant-wide control loop performance assessment,” Computers and Chemical Engineering, vol. 31, no. 2, pp. 86–99, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Rahman and M. A. A. Shoukat Choudhury, “Detection of control loop interactions and prioritization of control loop maintenance,” Control Engineering Practice, vol. 19, no. 7, pp. 723–731, 2011. View at Publisher · View at Google Scholar
  4. M. Iri, K. Aoki, E. O'Shima, and H. Matsuyama, “An algorithm for diagnosis of system failures in the chemical process,” Computers and Chemical Engineering, vol. 3, no. 1–4, pp. 489–493, 1979. View at Google Scholar · View at Scopus
  5. M. Iri, K. Aoki, E. O'shima, and H. Matsuyama, “A graphical approach to the problem of locating the origin of the system failure,” Journal of Operations Research Society of Japan, vol. 23, no. 4, pp. 295–311, 1980. View at Google Scholar
  6. N. F. Thornhill and A. Horch, “Advances and new directions in plant-wide disturbance detection and diagnosis,” Control Engineering Practice, vol. 15, no. 10, pp. 1196–1206, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. V. Venkatasubramanian, R. Rengaswamy, and S. N. Kavuri, “A review of process fault detection and diagnosis part II: qualitative models and search strategies,” Computers and Chemical Engineering, vol. 27, no. 3, pp. 313–326, 2003. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Wright, “Correlation and causation,” Journal of Agricultural Research, vol. 20, pp. 557–585, 1921. View at Google Scholar
  9. K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” NeuroImage, vol. 19, no. 4, pp. 1273–1302, 2003. View at Publisher · View at Google Scholar · View at Scopus
  10. R. S. H. Mah, Chemical Process Structures and Information Flows, Butterworth Publishers, Boston, Mass, USA, 1990.
  11. G. Simson, Data Modeling: Theory and Practice, Technics Publications LLC, Denville, NJ, USA, 2007.
  12. OMG, Business Semantics of Business Rules RFP. br/03-06-03, 2003.
  13. F. Yang and D. Y. Xiao, “Review of SDG modeling and its application,” Control Theory and Applications, vol. 22, no. 5, pp. 767–774, 2005. View at Google Scholar · View at Scopus
  14. F. Yang, D. Xiao, and S. L. Shah, “Qualitative fault detection and hazard analysis based on signed directed graphs for large-scale complex systems,” in Fault Detection, W. Zhang, Ed., pp. 15–50, IN-TECH, Vukovar, Croatia, 2010. View at Google Scholar
  15. M. R. Maurya, R. Rengaswamy, and V. Venkatasubramanian, “A systematic framework for the development and analysis of signed digraphs for chemical processes. 1. Algorithms and analysis,” Industrial and Engineering Chemistry Research, vol. 42, no. 20, pp. 4789–4810, 2003. View at Google Scholar · View at Scopus
  16. M. R. Maurya, R. Rengaswamy, and V. Venkatasubramanian, “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, vol. 42, no. 20, pp. 4811–4827, 2003. View at Google Scholar · View at Scopus
  17. H. M. Paynter, Analysis and Design of Engineering Systems, MIT Press, Cambridge, Mass, USA, 1960.
  18. P. J. Mosterman and G. Biswas, “Diagnosis of continuous valued systems in transient operating regions,” IEEE Transactions on Systems, Man, and Cybernetics Part A, vol. 29, no. 6, pp. 554–565, 1999. View at Google Scholar · View at Scopus
  19. L. Leyval, S. Gentil, and S. Feray-Beamont, “Model-based causal reasoning for process supervision,” Automatica, vol. 30, no. 8, pp. 1295–1306, 1994. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Montmain and S. Gentil, “Dynamic causal model diagnostic reasoning for online technical process supervision,” Automatica, vol. 36, no. 8, pp. 1137–1152, 2000. View at Publisher · View at Google Scholar · View at Scopus
  21. M. R. Maurya, R. Rengaswamy, and V. Venkatasubramanian, “A signed directed graph and qualitative trend analysis-based framework for incipient fault diagnosis,” Chemical Engineering Research and Design, vol. 85, no. 10, pp. 1407–1422, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. D. Gao, C. Wu, B. Zhang, and X. Ma, “Signed directed graph and qualitative trend analysis based fault diagnosis in chemical industry,” Chinese Journal of Chemical Engineering, vol. 18, no. 2, pp. 265–276, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Cheng, V.-M. Tikkala, A. Zakharov, T. Myller, and S. L. Jamsa-Jounela, “Application of the enhanced dynamic causal digraph method on a three-layer board machine,” IEEE Transactions on Control Systems Technology, vol. 19, no. 3, pp. 644–655, 2011. View at Publisher · View at Google Scholar
  24. C. J. Alonso, C. Llamas, J. A. Maestro, and B. Pulido, “Diagnosis of dynamic systems: a knowledge model that allows tracking the system during the diagnosis process,” Lecture Notes in Artificial Intelligence, vol. 2718, pp. 208–218, 2003. View at Google Scholar
  25. J. Pastor, M. Lafon, L. Trave-Massuyes, J.-F. Demonet, B. Doyon, and P. Celsis, “Information processing in large-scale cerebral networks: the causal connectivity approach,” Biological Cybernetics, vol. 82, no. 1, pp. 49–59, 2000. View at Google Scholar · View at Scopus
  26. I. Fagarasan, S. Ploix, and S. Gentil, “Causal fault detection and isolation based on a set-membership approach,” Automatica, vol. 40, no. 12, pp. 2099–2110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. J. Aslund, J. Biteus, E. Frisk, M. Krysander, and L. Nielsen, “Safety analysis of autonomous systems by extended fault tree analysis,” International Journal of Adaptive Control and Signal Processing, vol. 21, no. 2-3, pp. 287–298, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. F. Yang, S. L. Shah, and D. Xiao, “Signed directed graph based modeling and its validation from process knowledge and process data,” International Journal of Applied Mathematics and Computer Science, vol. 22, no. 1, pp. 387–392, 2012. View at Google Scholar
  29. M. A. Kramer and B. L. Palowitch, “A rule-based approach to fault diagnosis using the signed directed graph,” AIChE Journal, vol. 33, no. 7, pp. 1067–1078, 1987. View at Google Scholar · View at Scopus
  30. C.-C. Chang and C.-C. Yu, “On-line fault diagnosis using the signed directed graph,” Industrial and Engineering Chemistry Research, vol. 29, no. 7, pp. 1290–1299, 1990. View at Google Scholar · View at Scopus
  31. J. Thambirajah, L. Benabbas, M. Bauer, and N. F. Thornhill, “Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history,” Computers and Chemical Engineering, vol. 33, no. 2, pp. 503–512, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Bauer and N. F. Thornhill, “A practical method for identifying the propagation path of plant-wide disturbances,” Journal of Process Control, vol. 18, no. 7-8, pp. 707–719, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. S. L. Bressler and A. K. Seth, “Wiener-Granger causality: a well established methodology,” NeuroImage, vol. 58, no. 2, pp. 323–329, 2011. View at Publisher · View at Google Scholar
  34. M. J. Kaminski and K. J. Blinowska, “A new method of the description of the information flow in the brain structures,” Biological Cybernetics, vol. 65, no. 3, pp. 203–210, 1991. View at Publisher · View at Google Scholar · View at Scopus
  35. L. A. Baccala and K. Sameshima, “Partial directed coherence: a new concept in neural structure determination,” Biological Cybernetics, vol. 84, no. 6, pp. 463–474, 2001. View at Google Scholar · View at Scopus
  36. S. Gigi and A. K. Tangirala, “Quantitative analysis of directional strengths in jointly stationary linear multivariate processes,” Biological Cybernetics, vol. 103, no. 2, pp. 119–133, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. L. Faes, A. Porta, and G. Nollo, “Testing frequency-domain causality in multivariate time series,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 8, Article ID 5416292, pp. 1897–1906, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. T. Schreiber, “Measuring information transfer,” Physical Review Letters, vol. 85, no. 2, pp. 461–464, 2000. View at Publisher · View at Google Scholar · View at Scopus
  39. M. Bauer, J. W. Cox, M. H. Caveness, J. J. Downs, and N. F. Thornhill, “Finding the direction of disturbance propagation in a chemical process using transfer entropy,” IEEE Transactions on Control Systems Technology, vol. 15, no. 1, pp. 12–21, 2007. View at Publisher · View at Google Scholar · View at Scopus
  40. R. G. Cowell, A. P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter, Probabilistic Networks and Expert Systems, Springer, New York, NY, USA, 1999.
  41. F. Yang and D. Xiao, “Model and fault inference with the framework of probabilistic SDG,” in Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision (ICARV '06), pp. 1023–1028, Singapore, 2006.
  42. T. Richardson and P. Spirtes, “Automated discovery of linear feedback models,” in Computation, Causation, and Causality, C. Glymour and G. Cooper, Eds., MIT Press, 2001. View at Google Scholar
  43. U. Feldmann and J. Bhattacharya, “Predictability improvement as an asymmetrical measure of interdependence in bivariate time series,” International Journal of Bifurcation and Chaos, vol. 14, no. 2, pp. 505–514, 2004. View at Publisher · View at Google Scholar · View at Scopus
  44. M. Bauer and N. F. Thornhill, “Measuring cause and effect between process variables,” in Proceedings of the the IEEE Advanced Process Control Applications for Industry Workshop, Vancouver, Canada, May 2005.
  45. S. M. Smith, K. L. Miller, G. Salimi-Khorshidi et al., “Network modelling methods for FMRI,” NeuroImage, vol. 54, no. 2, pp. 875–891, 2011. View at Publisher · View at Google Scholar · View at Scopus
  46. M. Lungarella, K. Ishiguro, Y. Kuniyoshi, and N. Otsu, “Methods for quantifying the causal structure of bivariate time series,” International Journal of Bifurcation and Chaos, vol. 17, no. 3, pp. 903–921, 2007. View at Publisher · View at Google Scholar · View at Scopus
  47. Z. Q. Zhang, C. G. Wu, B. K. Zhang, T. Xia, and A. F. Li, “SDG multiple fault diagnosis by real-time inverse inference,” Reliability Engineering and System Safety, vol. 87, no. 2, pp. 173–189, 2005. View at Publisher · View at Google Scholar · View at Scopus
  48. O. O. Oyeleye and M. A. Kramer, “Qualitative simulation of chemical process systems: steady-state analysis,” AIChE Journal, vol. 34, no. 9, pp. 1441–1454, 1988. View at Google Scholar · View at Scopus
  49. V. Venkatasubramanian, J. Zhao, and S. Viswanathan, “Intelligent systems for HAZOP analysis of complex process plants,” Computers and Chemical Engineering, vol. 24, no. 9-10, pp. 2291–2302, 2000. View at Publisher · View at Google Scholar · View at Scopus
  50. F. Yang and D. Xiao, “Probabilistic signed directed graph and its application in hazard assessment,” in Progress in Safety Science and Technology, P. Huang, Y. Wang, and S. Li, Eds., vol. 6, pp. 111–115, Science Press, Beijing, China, 2006. View at Google Scholar
  51. F. Yang, S. Shah, and D. Xiao, “SDG model-based analysis of fault propagation in control systems,” in Proceedings of the 22nd Canadian Conference on Electrical and Computer Engineering (CCECE '09), pp. 1152–1157, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  52. S. Gentil and J. Montmain, “Hierarchical representation of complex systems for supporting human decision making,” Advanced Engineering Informatics, vol. 18, no. 3, pp. 143–159, 2004. View at Publisher · View at Google Scholar · View at Scopus
  53. F. Yang and D. Xiao, “Hierarchical description of SDG model and its fault inference,” in Proceedings of the Seminar on Production Safety and Control in Petrochemical Industry, pp. 1–5, Beijing, China, October 2006.
  54. J. Chen and J. Howell, “A self-validating control system based approach to plant fault detection and diagnosis,” Computers and Chemical Engineering, vol. 25, no. 2-3, pp. 337–358, 2001. View at Publisher · View at Google Scholar · View at Scopus
  55. F. Yang, Research on dynamic description and inference approaches in SDG model-based fault analysis, Ph.D. thesis, Tsinghua University, Beijing, China, 2008.
  56. J. Shiozaki, H. Matsuyama, E. O'Shima, and M. Iri, “An improved algorithm for diagnosis of system failures in the chemical process,” Computers and Chemical Engineering, vol. 9, no. 3, pp. 285–293, 1985. View at Google Scholar · View at Scopus
  57. H. Vedam and V. Venkatasubramanian, “Signed digraph based multiple fault diagnosis,” Computers and Chemical Engineering, vol. 21, supplement 1, pp. S655–S660, 1997. View at Google Scholar · View at Scopus
  58. C. C. Han, R. F. Shih, and L. S. Lee, “Quantifying signed directed graphs with the fuzzy set for fault diagnosis resolution improvement,” Industrial and Engineering Chemistry Research, vol. 33, no. 8, pp. 1943–1954, 1994. View at Google Scholar · View at Scopus
  59. K. Takeda, B. Shibata, Y. Tsuge, and H. Matsuyama, “Improvement of fault diagnostic system utilizing signed directed graph–the method using transfer delay of failure,” Transactions of the Society of Instrument and Control Engineers, vol. 31, no. 1, pp. 98–107, 1995. View at Google Scholar
  60. F. Yang and D. Xiao, “Approach to fault diagnosis using SDG based on fault revealing time,” in Proceedings of the 6th World Congress on Intelligent Control and Automation (WCICA '06), pp. 5744–5747, Dalian, China, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  61. F. Yang and D. Xiao, “SDG-based fault isolation for large-scale complex systems solved by rough set theory,” in Proceedings of the 17th IFAC World Congress, pp. 7221–7226, Seoul, Korea, 2008. View at Publisher · View at Google Scholar
  62. H. Jiang, R. Patwardhan, and S. L. Shah, “Root cause diagnosis of plant-wide oscillations using the concept of adjacency matrix,” Journal of Process Control, vol. 19, no. 8, pp. 1347–1354, 2009. View at Publisher · View at Google Scholar · View at Scopus
  63. R. J. Doyle, S. A. Chien, U. M. Fayyad, and E. J. Wyatt, “Focused real-time systems monitoring based on multiple anomaly models,” in Proceedings of the 7th International Workshop on Qualitative Reasoning About Physical Systems, pp. 75–82, Eastsound, WA, USA, May 1993.
  64. J. Chen and J. Howell, “Towards distributed diagnosis of the Tennessee Eastman process benchmark,” Control Engineering Practice, vol. 10, no. 9, pp. 971–987, 2002. View at Publisher · View at Google Scholar · View at Scopus