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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.

Keywords

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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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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