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전류센서를 이용한 BLDC 전동기 권선 결함 검출 방법 비교

Comparison of Fault Detection Methods for the BLDC Motor Using the Current Sensor

  • 이재현 (한국해양대학교 기관시스템공학부)
  • 투고 : 2010.09.28
  • 심사 : 2010.11.25
  • 발행 : 2010.11.30

초록

권선 결함을 검출하기 위하여 다양한 방법들이 적용되어 왔는데, 최근에는 전류 신호를 이용한 결함 검출 방법에 대한 연구들이 많이 이뤄지고 있다. 전류 신호는 전동기의 권선 결함에 대한 주요한 정보를 담고 있으며 본 연구에서도 BLDC 전동기의 권선 단락을 검출하기 위하여 전류 신호로부터 결함 특징을 추출하는 방법을 적용하였다. 본 연구를 통해 전류 신호로부터 결함 특징을 추출하는데 가장 적절한 방법을 시뮬레이션 및 실제 측정 데이터를 통해 비교 평가하고자 하였다.

Several methods have been applied to detect winding faults (turn-to-turn short). The representative approaches have been focusing on current signals. The current signal 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. Therefore, it is necessary to select proper feature extraction methods among the popular methods that use current signals.

키워드

참고문헌

  1. P. Vas, Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines, Clarendron Press, Oxford, 1993.
  2. G. B. Kliman and J. Stein, "Induction motor fault detection via passive current monitoring," International Conference in Electrical Machines, Cambridge, MA, pp. 13-17, August 1990.
  3. Y. E. Zhongming and W. U. Bin, "A review on induction motor online fault diagnosis," The Third International Power Electronics and Motion Control Conference (PIEMC 2000), vol. 3, pp. 1353-1358, Aug. 15-18, 2000
  4. K. Abbaszadeh, J. Milimonfared, M. Haji, and H. A. Toliyat, "Broken bar detection in induction motor via Wavelet transformation," IECON'01: The 27th Annual Conference of the IEEE Industrial Electronics Society, pp. 95-99, 2001.
  5. M. Haji and H. A. Toliyat, "Pattern recognition-A technique for induction machines rotor fault detection eccentricity and broken bar fault," Conference Record of the 2001 IEEE Industry Applications Conference, vol. 3, pp. 1572-1578, 2001.
  6. S. Nandi, H. A. Toliyat, "Condition monitoring and fault diagnosis of electrical machines - A review," IEEE Industry Applications Conference, vol. 1, pp. 197-204, 1999.
  7. B. Yazici, G. B. Kliman, "An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current," IEEE Trans. on Industry Application, vol. 35, no. 2, pp. 442-452, 1999. https://doi.org/10.1109/28.753640
  8. S. G. Tzafestas (Ed.), Applications of Intelligent Control to Engineering Systems: Chapter 4 Particle Filter Based Anomaly Detection for Aircraft Actuator Systems. Springer, vol. 39, pp. 65-88, 2009.
  9. Hamid Nejjari and Mohamed El Hachemi Benbouzid, "Monitoring and diagnosis of induction motors electrical faults using a current Park's vector pattern learning approach", IEEE Transactions on Industry Applications, vol. 36, no. 3, pp. 730-735, 2000 https://doi.org/10.1109/28.845047
  10. Hyeon Bae, Sung-Shin Kim, George Vachtsevanos, "Fault detection and diagnosis of winding short in BLDC motors based on fuzzy similarity," International Journal of Fuzzy Logic and Intelligent systems, vol. 9, no. 2, pp. 99-104, July 2009. https://doi.org/10.5391/IJFIS.2009.9.2.099