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Technology Extraction of Expert Operator Skills from Process Time Series Data

  • Setsuya Kurahashi
  • Takao Terano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4998)

Abstract

Continuation processes in chemical and/or biotechnical plants always generate a large amount of time series data. However, since conventional process models are described as a set of control models, it is difficult to explain complicated and active plant behaviors. To uncover complex plant behaviors, this paper proposes a new method of developing a process response model from continuous time-series data. The method consists of the following phases: (1) Reciprocal correlation analysis; (2) Process response model; (3) Extraction of control rules; (4) Extraction of a workflow; and (5) Detection of outliers. The main contribution of the research is to establish a method to mine a set of meaningful control rules from a Learning Classifier System using the Minimum Description Length criteria and Tabu search method. The proposed method has been applied to an actual process of a biochemical plant and has shown its validity and effectiveness.

Keywords

Time Series Data Improvement Rate Distillation Tower Tabu List Control Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Rissanen, J.: Modeling by shortest data description. Automatica 14, 465–471 (1978)CrossRefzbMATHGoogle Scholar
  2. 2.
    Adami, C.: Introduction to Artificial Life. Springer, NY (1998)CrossRefzbMATHGoogle Scholar
  3. 3.
    Mehta, M., Rissanen, J., Agrawal, R.: MDL-based decision tree pruning. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD 1995), pp. 216–221 (1995)Google Scholar
  4. 4.
    Yamanishi, K.: A learning criterion for stochastic rules. Machine Learning 8, 165–203 (1992)zbMATHGoogle Scholar
  5. 5.
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of assosiation rules. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press and The MIT Press (1996)Google Scholar
  6. 6.
    Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measures of Interest. Kluwer Academic Publishers, Dordrecht (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. SIGART Bull. (63), 49 (1977)Google Scholar
  8. 8.
    Smith, S.: A learning system based on genetic adaptive algorithms. In: Ph.D thesis. University of Pittsburgh (1980)Google Scholar
  9. 9.
    Smith, S.: Flexible learning of problem solving heuristics through adaptive search. In: Proceedings 8th International Joint Conference on Artificial Intelligence (August 1983)Google Scholar
  10. 10.
    Butz, M.V., Pelikan, M., Llorà, X., Goldberg, D.E.: Extracted global structure makes local building block processing effective in XCS. In: GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 655–662. ACM, New York (2005)Google Scholar
  11. 11.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Inc., Reading (1989)zbMATHGoogle Scholar
  12. 12.
    Adriaans, P., Zantinge, D.: Data Mining. Addison-Wesley, Reading (1996)Google Scholar
  13. 13.
    Weiss, S.M., Indurkhya, N.: Predictice Data Mining, A Practical Guide. Morgan Kaufmann Publishers, Inc., San Francisco (1997)zbMATHGoogle Scholar
  14. 14.
    Berndt, D.J., Clifford, J.: Finding patterns in time series: a dynamic programming approach, 229–248 (1996)Google Scholar
  15. 15.
    Hetland, M.L., Saetrom, P.: Evolutionary rule mining in time series databases. Mach. Learn. 58(2-3), 107–125 (2005)CrossRefzbMATHGoogle Scholar
  16. 16.
    Harvey, A.C.: Time Series Models. Prentice Hall/Harvester (1993)Google Scholar
  17. 17.
    Stock, J.H., Watson, M.W.: A probability model of the coincident economic indicators. Working Paper 2772, National Bureau of Economic Research (November 1988)Google Scholar
  18. 18.
    Freitas, A.A.: Data mining and knowledge discovery with evolutionary algorithms. Springer, Heidelberg (2002)CrossRefzbMATHGoogle Scholar
  19. 19.
    Barry, A., Holme, J., Llora, X.: Data mining using learning classifier systems. In: Bull, L. (ed.) Applications of Learning Classifier Systems, pp. 15–67. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    Iba, H., de Garis, H., Sato, T.: Genetic programming using a minimum description length principle. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, pp. 265–284. MIT Press, Cambridge (1994)Google Scholar
  21. 21.
    Bacardit, J., Garrell, J.M.: Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2003. LNCS (LNAI), vol. 4399, pp. 59–79. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Smyth, P., Goodman, R.M.: An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering 4(4), 301–316 (1992)CrossRefGoogle Scholar
  23. 23.
    Hilderman, R.J., Hamilton, H.J.: Heuristic measures of interestingness. In: Proceedings of the Third European Conference on the Principles of Data Mining and Knowledge Discovery, pp. 232–241 (1999)Google Scholar
  24. 24.
    Quinlan, J.R.: C4.5:Programs for Machine Learning. Morgan Kaufman Publishers, Inc., San Francisco (1993)Google Scholar
  25. 25.
    Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Dordrecht (1997)CrossRefzbMATHGoogle Scholar
  26. 26.
    Yamanishi, K., Takeuchi, J.: A unifying approach to detecting outliers and change-points from nonstationary data. In: The Eighth ACM SIGKDD(KDD2002) (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Setsuya Kurahashi
    • 1
  • Takao Terano
    • 2
  1. 1.University of TsukubaTokyoJapan
  2. 2.Tokyo Institute of TechnologyYokohamaJapan

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