KIEE International Transaction on Electrical Machinery and Energy Conversion Systems

대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

Monolith and Partition Schemes with LDA and Neural Networks as Detector Units for Induction Motor Broken Rotor Bar Fault Detection

  • Ayhan Bulent (Dept. of Electrical and Computer Engineering North Carolina State University) ;
  • Chow Mo-Yuen (Dept. of Electrical and Computer Engineering North Carolina State University) ;
  • Song Myung-Hyun (Dept. of Electrical and Computer Engineering North Carolina State University)
  • 발행 : 2005.06.01
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초록

Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor current spectrum. Broken rotor bar fault detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple Discriminant Analysis (MDA) and Artificial Neural Networks (ANN) provide appropriate environments to develop such fault detection schemes because of their multi-input processing capabilities. This paper describes two fault detection schemes for broken rotor bar fault detection with multiple signature processing, and demonstrates that multiple signature processing is more efficient than single signature processing.

키워드

참고문헌

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