Fault Diagnosis in a Power Generation Plant Using a Neural Fuzzy System with Rule Extraction

  • Kok Yeng Chen
  • Chee Peng Lim
  • Weng Kin Lai
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

9.5. Summary

In this chapter, FMM is endowed with a rule extraction algorithm. With the rule extraction algorithm, FMM is able to explain its predictions using fuzzy if-then rules, thus overcoming the “black-box” phenomenon as suffered by most NN models. Applicability of FMM to fault diagnosis tasks in a power generation plant has been examined. The potential of FMM in learning and predicting faults in complex processes as well as in providing a comprehensible explanation for its predictions has been demonstrated in two experiments. The proposed rule extraction algorithm is able to yield a comprehensible rule set. The extracted rules have been verified as meaningful and are in line with the domain knowledge as well as experts’ opinions. Further research work will concentrate on the aspects of implementation, validation, and verification of FMM as a useful, robust, and intelligent fault diagnosis tool in a variety of application domains.


Fault Diagnosis Rule Extraction Heat Transfer Condition Condenser Tube Power Generation Plant 
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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  1. 1.
    Al-Najjar B (1996) Total quality maintenance: An approach for continuous reduction in costs of quality products. Journal of Quality in Maintenance Engineering 2:2–20CrossRefGoogle Scholar
  2. 2.
    Andrews R, Diederich J and Tickle AB (1995) A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge Based Systems 8:373–389CrossRefGoogle Scholar
  3. 3.
    Carpenter GA and Tan AH (1995) Rule extraction: From neural architecture to symbolic representation. Connection Science 7:3–27Google Scholar
  4. 4.
    Craven MW and Shavlik JW (1994) Using sampling and queries to extract rules from trained neural networks. In: Machine Learning: Proceedings of the Eleventh International Conference, San Francisco, CA, USAGoogle Scholar
  5. 5.
    Dellomo MR (1999) Helicopter gearbox fault detection: a neural network based approach. Journal of Vibration and Acoustics 121:265–272Google Scholar
  6. 6.
    Denny G (1993) F16 jet engine trending and diagnostics with neural networks. In: Proceedings of SPIE, vol. 1965, pp. 419–412Google Scholar
  7. 7.
    Dietz WE, Kiech EL and Ali M (1989) Jet and rocket engine fault diagnosis in real time. Journal of Neural Network Computing 1: 5–18Google Scholar
  8. 8.
    de Efron B (1979) Bootstrap Methods. Another Look at the Jackknife. The Annals of Statistics 7:1–26MathSciNetGoogle Scholar
  9. 9.
    Efron B and Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & HallGoogle Scholar
  10. 10.
    Freitas JFG, MacLeod IM and Maltz JS (1999) Neural networks for pneumatic actuator fault detection. Transactions of the SAIEE 90:28–34Google Scholar
  11. 11.
    Fu LM (1994) Rule generation from neural networks. IEEE Transactions on Systems, Man, and Cybernetics 28: 1114–1124Google Scholar
  12. 12.
    Jack LB, Nandi AK and McCormick AC (1999) Diagnosis of rolling element bearing faults using radial basis function networks. EURASIP Journal on Applied Signal Processing 6:25–32Google Scholar
  13. 13.
    Kuo RJ (1995) Intelligent diagnosis for turbine blade faults using artificial neural networks and fuzzy logic. Engineering Applications of Artificial Intelligence 8:25–34CrossRefGoogle Scholar
  14. 14.
    Polycarpou MM and Helmicki AJ (1995) Automated fault detection and accommodation: A learning system approach. IEEE Transactions on System, Man, and Cybernetics 25:1447–1458CrossRefGoogle Scholar
  15. 15.
    Simpson P (1992) Fuzzy Min-Max Neural Networks-Part 1: Classification. IEEE Transactions on Neural Networks 3:776–786CrossRefGoogle Scholar
  16. 16.
    Sharkey JC, Chandroth JO and Sharkey NE (2000) A multi-net system for the fault diagnosis of a diesel engine. Neural Computing and Applications 9:152–160CrossRefGoogle Scholar
  17. 17.
    System description and operating procedures, Prai Power Station Stage 3, vol. 14, 1999Google Scholar
  18. 18.
    Tickle B, Orlowski M and Diederich J (1996) DEDEC: A methodology for extracting rule from trained artificial neural networks. In: Proceedings of the Rule Extraction from Trained Artificial Neural Network Workshop, Society for the Study of Artificial Intelligence and Simulation of Behaviour Workshop Series (AISB’96), University of Sussex, Brighton, UK, pp. 90–102, 1996Google Scholar
  19. 19.
    Venkatasubramanian V, Rengaswamy R, Yin K and Kavuri SN (2003a) A review of process fault detection and diagnosis. Part I: Quantitative model-based methods. Computers and Chemical Engineering 27:293–311CrossRefGoogle Scholar
  20. 20.
    Venkatasubramanian V, Rengaswamy R, Yin K and Kavuri SN (2003b) A review of process fault detection and diagnosis. Part II: Qualitative models and search strategies. Computers and Chemical Engineering 27:313–326CrossRefGoogle Scholar
  21. 21.
    Venkatasubramanian V, Rengaswamy R, Yin K and Kavuri SN (2003c) A review of process fault detection and diagnosis. Part III: Process history based methods. Computers and Chemical Engineering 27:327–346CrossRefGoogle Scholar
  22. 22.
    Kohonen T (1984) Self-Organization and Associative Memory. Springer-Verlag BerlinzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  • Kok Yeng Chen
    • 1
  • Chee Peng Lim
    • 1
  • Weng Kin Lai
    • 2
  1. 1.School of Electrical and Electronic EngineeringUniversity of Science MalaysiaNibong Tebal, PenangMalaysia
  2. 2.MIMOS BerhadTechnology Park MalaysiaKuala LumpurMalaysia

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