International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Fault Detection and Diagnosis of Winding Short in BLDC Motors Based on Fuzzy Similarity

  • Bae, Hyeon (School of Electrical and Computer Engineering, Georgia Institute of Technology) ;
  • Kim, Sung-Shin (School of Electrical and Computer Engineering, Pusan National University) ;
  • Vachtsevanos, George (School of Electrical and Computer Engineering, Georgia Institute of Technology)
  • Received : 2009.01.14
  • Accepted : 2009.05.25
  • Published : 2009.06.30
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Abstract

The turn-to-turn short is one major fault of the motor faults of BLDC motors and can appear frequently. When the fault happens, the motor can be operated without breakdown, but it is necessary to maintain the motor for continuous working. In past research, several methods have been applied to detect winding faults. The representative approaches have been focusing on current signals, which can give important information to extract features and to detect faults. In this study, current sensors were installed to measure signals for fault detection of BLDC motors. In this study, the Park's vector method was used to extract the features and to isolate the faults from the current measured by sensors. Because this method can consider the three-phase current values, it is useful to detect features from one-phase and three-phase faults. After extracting two-dimensional features, the final feature was generated by using the two-dimensional values using the distance equation. The values were used in fuzzy similarity to isolate the faults. Fuzzy similarity is an available tool to diagnose the fault without model generation and the fault was converted to the percentage value that can be considered as possibility of the fault.

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References

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