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A New Fault Detection Method of Induction Motor

  • Conference paper
Artificial Intelligence and Computational Intelligence (AICI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6320))

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Abstract

According to the shortcoming that the Extended Kalman filter (EKF) method can only estimate the speed and rotor position of induction motors in time domain when it is used to diagnose the fault existed in induction motor. A new multi-scale default diagnosing method is developed by combining EKF and wavelet transform. By monitoring the voltages and currents of the stator, it is possible to estimate the speed and position on-line. The new filter combines the merit of EKF and wavelet, and it not only possesses the multiscale analysis capability both in time domain and frequency domain, but also has better estimation accuracy than traditional EKF. Computer simulation shows the effect of the new algorithm.

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Author information

Authors and Affiliations

  1. Electric Engineering School, Shanghai Dianji University, Shanghai, China

    Chuanbo Wen

  2. Electronic and information school, Shanghai Dianji University, Shanghai, China

    Yun Liang

Authors
  1. Chuanbo Wen
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  2. Yun Liang
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Editor information

Editors and Affiliations

  1. Department of Business Administration, Caritas Francis Hsu College, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

  2. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  3. Department of Computer Science, Nanjing University, 210093, Nanjing, China

    Yang Gao

  4. School of Computer, Nanjing University of Posts and Telecommunications, 210003, Nanjing, China

    Jingsheng Lei

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© 2010 Springer-Verlag Berlin Heidelberg

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Wen, C., Liang, Y. (2010). A New Fault Detection Method of Induction Motor. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16527-6_1

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  • DOI: https://doi.org/10.1007/978-3-642-16527-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16526-9

  • Online ISBN: 978-3-642-16527-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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