Journal of Electrical Engineering and Technology

  • 제8권2호
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  • Pages.288-294
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  • 2013
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  • 1975-0102(pISSN)
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  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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MUSIC-based Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors Using Flux Signal

  • Youn, Young-Woo (HVDC Research Division, Korea Electrotechnology Research Institute (KERI)) ;
  • Yi, Sang-Hwa (HVDC Research Division, Korea Electrotechnology Research Institute (KERI)) ;
  • Hwang, Don-Ha (HVDC Research Division, Korea Electrotechnology Research Institute (KERI)) ;
  • Sun, Jong-Ho (HVDC Research Division, Korea Electrotechnology Research Institute (KERI)) ;
  • Kang, Dong-Sik (HVDC Research Division, Korea Electrotechnology Research Institute (KERI)) ;
  • Kim, Yong-Hwa (Department of Electronic Engineering, Myongji University)
  • 투고 : 2012.03.22
  • 심사 : 2012.08.10
  • 발행 : 2013.03.01
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초록

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

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참고문헌

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피인용 문헌

  1. Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network vol.2016, 2016, https://doi.org/10.1155/2016/7129376
  2. Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis vol.379, 2016, https://doi.org/10.1016/j.jsv.2016.05.035
  3. Robust Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors vol.9, pp.1, 2014, https://doi.org/10.5370/JEET.2014.9.1.037