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Fourier and Wavelet Transformations for the Fault Detection of Induction Motor with Stator Current

  • Sang-Hyuk Lee
  • Seong-Pyo Cheon
  • Yountae Kim
  • Sungshin Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

In this literature, fault detection of an induction motor is carried out using the information of stator current. After preprocessing actual data, Fourier and Wavelet transforms are applied to detect characteristics under the healthy and various faulted conditions. The most reliable phase current among 3-phase currents is selected by the fuzzy entropy. Data are trained with a neural network system, and the fault detection algorithm is carried out under the unknown data. The results of the proposed approach based on Fourier and Wavelet transformations show that the faults are properly classified into six categories.

Keywords

Fault Detection Wavelet Transformation Induction Motor Induction Machine Fuzzy Entropy 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sang-Hyuk Lee
    • 1
  • Seong-Pyo Cheon
    • 1
  • Yountae Kim
    • 1
  • Sungshin Kim
    • 1
  1. 1.School of Electrical and Computer Engineering, Pusan National University, Changjeon-dong, Geumjeong-gu, Busan 609-735Korea

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