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Fault Diagnosis for the Feedwater Heater System of a 300MW Coal-Fired Power Generating Unit Based on RBF Neural Network

  • Liangyu Ma
  • Yongguang Ma
  • Jin Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)

Abstract

In this paper, a new style radial basis function (RBF) neural network is used for fault diagnosis of the high-pressure feed-water heater system of a coal-fired power generating unit. The structure of the RBF network and its training algorithm are given. Another important factor to realize neural network based fault diagnosis, fault symptom fuzzy calculating methods for two different fault symptoms and their integrated calculation, are discussed in detail. The high-pressure feed-water heater system of a 300MW coal-fired power generating unit is taken as a fault diagnosis example. The fault knowledge library of the system is summarized. The fault diagnosis is further realized based on the above RBF neural network. It is shown that good diagnostic results can be acquired with RBF neural network method by using the fault fuzzy knowledge library of the high-pressure heater system.

Keywords

Radial Basis Function Fault Diagnosis Radial Basis Function Neural Network Radial Basis Function Network Orthogonal Little Square 
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.
    Leonard, J.A., Kramer, M.A.: Radial basis function networks for classifying process fault. IEEE control systems, 31–37 (April 1991)Google Scholar
  2. 2.
    Narendra, K.G., Sood, V.K., Khorasani, K., Patel, R.: Application of a radial basis function(RBF) neural network for fault diagnosis in a HVDC system. IEEE Trans. on power systems 13(1), 177–183 (1998)CrossRefGoogle Scholar
  3. 3.
    Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function network. IEEE trans. on neural networks 2(2), 302–309 (1991)CrossRefGoogle Scholar
  4. 4.
    Liangyu, M., Jianqiang, G., Yongguang, M.: Fault intelligent diagnosis for high-pressure feed-water heater system of a 300MW coal-fired power unit based on improved BP neural network. In: 2002 IEEE/PES international conference on power system technology proceedings, Kunming, vol. 3, pp. 1535–1539 (2002)Google Scholar
  5. 5.
    Liangyu, M., Bingshu, W., Jianqiang, G.: A new intelligent approach to diagnose the slight and incipient fault during the production process. Proceedings of the CSEE 22(6), 115–118 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Liangyu Ma
    • 1
  • Yongguang Ma
    • 1
  • Jin Ma
    • 1
  1. 1.School of Control Science and EngineeringNorth China Electric Power UniversityBaodingChina

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