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A Diagnostic Knowledge Model of Wind Turbine Fault

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Intelligent Robotics and Applications (ICIRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10464))

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Abstract

With the development of the wind power industry, wind power has become one of the main green generation energy. At the same time, with the wind power installed capacity increasing, the failure rate gradually growth. As wind turbine is a complex electromechanical equipment, the fault diagnosis for this kind of equipment is also a complicated process. Focused on the current shortage of fault diagnosis knowledge representation, this paper proposes a diagnostic knowledge model for wind turbine and also elaborates the model structure definition with a target to ensure the accuracy of fault diagnosis. Besides, this model can also offer assistance reference model for researchers in related fields to develop advanced methods for sharing and reuse of diagnostic knowledge.

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Author information

Authors and Affiliations

  1. Huadian Electric Power Research Institute, Hangzhou, 310030, China

    Hongwei Wang & Zhanli Liu

  2. University of Portsmouth, Portsmouth, PO1 3DJ, UK

    Hongwei Wang & Wei Liu

Authors
  1. Hongwei Wang
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  2. Wei Liu
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  3. Zhanli Liu
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Corresponding author

Correspondence to Wei Liu .

Editor information

Editors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    YongAn Huang

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Hao Wu

  3. Institute of Industrial Research, University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  4. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

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Wang, H., Liu, W., Liu, Z. (2017). A Diagnostic Knowledge Model of Wind Turbine Fault. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10464. Springer, Cham. https://doi.org/10.1007/978-3-319-65298-6_40

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  • DOI: https://doi.org/10.1007/978-3-319-65298-6_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65297-9

  • Online ISBN: 978-3-319-65298-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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