Advertisement

Bayesian Networks in Decision Support

  • Tomas O. Carlsson
  • Ronald Wennersten
Part of the Power Systems book series (POWSYS)

Abstract

Since the beginning of industrialism, the complexity of industrial processes has been continuously increasing. In modem plants during normal operation this complexity is handled by the computerised control system. However, some process conditions outside the design of the control system still have to be managed by the human operator. These conditions can be due to equipment malfunction, unknown inputs and disturbances. Due to the complexity and the limited direct process interaction that operators experience, they often have difficulties making the right decisions in these abnormal situations.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Berec L (1998) A multi-model method to fault detection and diagnosis: Bayesian solution. An introductory treatise. International Journal of Adaptive Control and Signal Processing 12: 81–92MathSciNetzbMATHCrossRefGoogle Scholar
  2. Bickford RL, Bickmore, TW, Caluori VA (1997) Real-time sensor validation for autonomous flight control. AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 33rd, pp 1–10Google Scholar
  3. Carlsson TO, Wennersten R (2001a) Graphical process models for risk assessment and operator decision support. In: 10th International symposium on Loss prevention and safety promotion in the process industries. Elsevier, Amsterdam, pp 699–711Google Scholar
  4. Carlsson TO, Wennersten R (2001b) Sensor and actuator fault diagnosis with a Bayesian network based on dynamic linear models. Submitted to: Computers and Chemical EngineeringGoogle Scholar
  5. Catino CA, Grantham SD, Ungar LH (1991) Automatic generation of qualitative models of chemical process unit. Computers and Chemical Engineering 15: 583–599CrossRefGoogle Scholar
  6. Chen J, Patton RJ (1999) Robust model-based fault diagnosis for dynamic systems. Kluwer Academic, LondonzbMATHCrossRefGoogle Scholar
  7. Choi SS, Kang KS, Kim HG, Chang SH (1995) Development of an on-line fuzzy expert system for integrated alarm processing in nuclear power plants. IEEE Transactions on nuclear science 42: 1406–1418CrossRefGoogle Scholar
  8. Chong HG and Walley WJ (1996) Rule-based versus probabilistic approaches to the diagnosis of faults in wastewater treatment processes. Artificial intelligence in engineering 1: 265–273CrossRefGoogle Scholar
  9. Downs JJ, Vogel EF (1993) A plant-wide industrial process control problem. Computers and Chemical Engineering 17: 245–255CrossRefGoogle Scholar
  10. Eide P, Maybeck P (1996) An MMAE failure detection system for the F-16. IEEE Transactions on Aerospace and electronic systems 32: 1125–1136CrossRefGoogle Scholar
  11. Fantoni PF (2000) Neuro-fuzzy model applied to full range signal validation of PWR nuclear power plant data. International Journal of General Systems 29: 305–320zbMATHCrossRefGoogle Scholar
  12. Frank, P.M. (1996) Analytical and qualitative model-based fault diagnosis — A survey and some new results. European J. of Control 2: 6–28zbMATHGoogle Scholar
  13. Friedman N, Murphy K, Russell S (1998) Learning the structure of dynamic probabilistic networks. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp 139–147Google Scholar
  14. Gardner RD, Harle DA (1998) Pattern discovery and specification techniques for alarm correlation. In: IEEE Network Operations and Management Symposium. Vol. 3, pp 713–722Google Scholar
  15. Gertler JJ. (1998) Fault detection and diagnosis in engineering systems. Marcel Dekker, New YorkGoogle Scholar
  16. Guo H, Crossman JA, Murphey YL, Coleman M (2000) Automotive signal diagnostics using wavelets and machine learning. IEEE Transactions on Vehicular Technology 49: 1650–1662CrossRefGoogle Scholar
  17. Horovitz E, Barry M (1995) Display of information for time-critical decision making. Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, pp 296–305Google Scholar
  18. Iri M, Aoki K, O’Shima E, Matsuyama H (1979) An algorithm for diagnosis of system failures in the chemical processes. Computers and Chemical Engineering 3: 489–493CrossRefGoogle Scholar
  19. Kirsch H, Kroschel K (1994) Applying Bayesian networks to fault diagnosis. In: Proceedings of the Third IEEE Conference on Control Applications 2, pp 895–900CrossRefGoogle Scholar
  20. Kramer M.A. (1987) Expert systems for process fault diagnosis: a general framework. In: Foundations of Computer Aided Process Operations. Proceedings of the First International Conference, pp 557–587Google Scholar
  21. Leung D, Romagnoli J (2000) Dynamic probabilistic model-based expert system for fault diagnosis. Computers and Chemical Engineering 24: 2473–2492CrossRefGoogle Scholar
  22. Ljung L (1999) System identification: theory for the user. Prentice Hall, N.J.Google Scholar
  23. MacGregor JF, Kourti T, Nomikos P (1996) Analysis, monitoring and fault diagnosis of industrial processes using multivariate statistical projection methods. In: IFAC 13a` Triennal World Congress M, pp 145–150Google Scholar
  24. McAvoy TJ, Ye N (1994) Base control for the Tennessee Eastman problem. Computers and Chemical Engineering 18: 383–413CrossRefGoogle Scholar
  25. Mo JK, Lee G, Nam DS, Yoon YH, Yoon ES (1997) Robust fault diagnosis based on clustered symptom trees. Control Eng. Practice 5: 199–208Google Scholar
  26. Murphy K (1998) Learning Switching Kalman Filter Models. Compaq Cambridge Research Lab Tech Report 98–10Google Scholar
  27. Murphy K (2001a) The Bayes Net Toolbox for Matlab. In: Computing Science and Statistics: Proceedings of Interface, 33: in press Google Scholar
  28. Murphy K (2001b) Bayes Net Toolbox for Matlab, University of California, Berkeley, http://www.cs.berkeley.edu/~murphyk/Bayes/bnt.html Google Scholar
  29. Nicholson AE, Brady JM (1994) Dynamic Belief networks for discrete monitoring. IEEE transactions on systems, man and cybernetics 24: 1593–1610CrossRefGoogle Scholar
  30. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, CAGoogle Scholar
  31. Russell S, Norvig P (1995) Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewoods NJzbMATHGoogle Scholar
  32. Shen Q, Leitch RR, Coghill GM (1993) Fuzzy qualitative modelling. In: IEE Colloquim on Two decades of fuzzy control-Part 2 Digest No. 1993/118, pp 3/1–4Google Scholar
  33. Tan JS, Kramer MA (1993) On-line hazard aversion: A Bayes approach. IEEE Transactions on Reliability 42: 317–326Google Scholar
  34. Teo CY, Kwok WK, Lee CC (1992) Application of AI techniques for fault diagnosis in power distribution system. In: First International Conference on Intelligent Systems Engineering, pp 53–58Google Scholar
  35. Tzafestas S, Watanabe K (1990) Modern approaches to system/sensor fault detection and diagnosis. Journal A 31: 42–57Google Scholar
  36. Vedam H, Venkatasubramanian V (1997) Signed digraph based multiple fault diagnosis. Computers and Chemical Engineering 21: S655 - S660Google Scholar
  37. Wennersten R, Narfeldt R, Gränfors A, Sjökvist S (1995) In: Proc. of the 11th symposium on Loss prevention and safety promotion in the process industries, pp 661–670.Google Scholar
  38. Wennersten R, Narfeldt R, Gränfors A, Sjökvist S (1996) Process modelling in fault diagnosis. Computers and Chemical Engineering 20: S665 - S6670CrossRefGoogle Scholar
  39. Willsky AS (1976) A survey of design methods for failure detection in dynamic systems. Automatica 12: 601–611MathSciNetzbMATHCrossRefGoogle Scholar
  40. Zhang J, Martin EB, Morris AJ (1996) Fault detection and diagnosis using multivariate statistical techniques. IChemE Transactions of the Institute of Chemical Engineers, Part A. Chemical Engineering Research and Design 74: 89–96Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Tomas O. Carlsson
    • 1
  • Ronald Wennersten
    • 1
  1. 1.Department of Chemical Engineering, Industrial Ecology and Process SafetyRoyal Institute of TechnologyStockholmSweden

Personalised recommendations