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Application of Wavelet Neural Network in the Fault Diagnosis of Turbine Generator Unit

  • Conference paper
Artificial Intelligence and Computational Intelligence (AICI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7530))

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Abstract

Wavelet neural network(WNN) is a type of feedforward network which is designed by using wavelet function as the activation functions in neural networks. Based on the technique of WNN, a diagnostic method is presented for turbine generator unit. The simulation results show that the proposed method can effectively diagnose the vibration fault of turbine generator, can overcome the random noise disturbance and has good application prospects.

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Author information

Authors and Affiliations

  1. School of Electronic and Information, Tongji University, Shanghai, 201804, China

    Chunmei Xu, Hao Zhang & Yuliang Qian

  2. School of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, 200090, China

    Chunmei Xu, Hao Zhang & Daogang Peng

Authors
  1. Chunmei Xu
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  2. Hao Zhang
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  3. Daogang Peng
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  4. Yuliang Qian
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Editor information

Editors and Affiliations

  1. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  2. Deaprtment of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

  3. School of Business Information Technology and Logistics, RMIT University, City Campus, 124 la Tobre Street, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  4. School of Electronics and Information, Tongji University, 201804, Shanghai, China

    Duoqian Miao

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© 2012 Springer-Verlag Berlin Heidelberg

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Xu, C., Zhang, H., Peng, D., Qian, Y. (2012). Application of Wavelet Neural Network in the Fault Diagnosis of Turbine Generator Unit. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_78

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  • DOI: https://doi.org/10.1007/978-3-642-33478-8_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33477-1

  • Online ISBN: 978-3-642-33478-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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