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Intelligent Built-in Test (BIT) for More-Electric Aircraft Power System Based on Hybrid Generalized LVQ Neural Network

  • Zhen Liu
  • Hui Lin
  • Xin Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

This paper proposes a hybrid neural network model based on the Generalized Learning Vector Quantization(GLVQ) learning algorithm and applies this proposed method to the BIT system of More-Electric Aircraft Electrical Power System (MEAEPS). This paper first discusses the feasibility of application unsupervised neural networks to the BIT system and the representative Generalized LVQ (GLVQ) neural network is selected due to its good performance in clustering analysis. Next, we adopt a new form of loss factor to modify the original GLVQ algorithm in order to make it more suitable for our application. Since unsupervised networks cannot distinguish the similar classes, we add a LVQ layer to the GLVQ network to construct a hybrid neural network model. Finally, the proposed method has been applied to the intelligent BIT system of the MEAEPS, and the results show that the proposed method is promising to improve the performance of the BIT system.

Keywords

Fault Diagnosis Fault Mode Hybrid Network Winner Node Unsupervised Neural Network 
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.
    Maldonado, M.A., Korba, G.J.: Power Management and Distribution System for a More Electric Aircraft (MADMEL). IEEE AES Magazine 14(12), 3–8 (1999)Google Scholar
  2. 2.
    Richards, D.W.: Smart BIT: A Plan for Intelligent Built-In Test. IEEE AES Magazine 4(1), 26–29 (1989)Google Scholar
  3. 3.
    Xu, Y.C., Wen, X.S., Yi, X.S., Tao, L.M.: New ART-2A Unsupervised Clustering Algorithm and Its Application on BIT Fault Diagnosis. Journal of Vibration Engineering 5(2), 167–172 (2002)Google Scholar
  4. 4.
    Wu, H.Q., Liu, Y., Ding, Y.L., Zhang, X.W.: Application Study of SOM Artificial Neural Net in Airliner Fault Diagnosis. Journal of Nanjing University of Aeronautics & Astronautics 34(1), 31–34 (2002)Google Scholar
  5. 5.
    Kohonen, T.: Self-Organization and Associative Memory, 3rd edn. Springer, Heidelberg (1989)Google Scholar
  6. 6.
    Pal, N.R., Bezdek, J.C., Tsao, E.C.-K.: Generalized Clustering Networks and Kohonen’s Self-organizing Scheme. IEEE Transactions on Neural Networks 4(4), 549–557 (1993)CrossRefGoogle Scholar
  7. 7.
    Karayiannis, N.B., Pai, P.-I.: Fuzzy Algorithm for Learning Vector Quantization. IEEE Transactions on Neural Networks 7(5), 1196–1211 (1996)CrossRefGoogle Scholar
  8. 8.
    Gonzalez, A.I., Grana, M., Anjou, A.D.: An Analysis of the GLVQ Algorithm. IEEE Transactions on Neural Networks 6(4), 1012–1016 (1995)CrossRefGoogle Scholar
  9. 9.
    Karayiannis, N.B.: A Methodology for Constructing Fuzzy Algorithms for Learning Vector Quantization. IEEE Transactions on Neural Networks 8(3), 505–518 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhen Liu
    • 1
  • Hui Lin
    • 1
  • Xin Luo
    • 1
  1. 1.College of AutomationNorthwestern Polytechnical UniversityXi’anChina

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