한국소음진동공학회논문집 (Transactions of the Korean Society for Noise and Vibration Engineering)

  • 제14권11호
  • /
  • Pages.1091-1100
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  • 2004
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  • 1598-2785(pISSN)
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  • 2287-5476(eISSN)
한국소음진동공학회 (The Korean Society for Noise and Vibration Engineering)
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진동 및 전류신호의 데이터융합을 이용한 유도전동기의 결함진단

Fault Diagnosis of Induction Motors Using Data Fusion of Vibration and Current Signals

  • 김광진 (부경대학교 대학원 기계공학부) ;
  • 한천 (부경대학교 대학원 기계공학부)
  • 발행 : 2004.11.01
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초록

This paper presents an approach for the monitoring and detection of faults in induction machine by using data fusion technique and Dempster-Shafer theory Features are extracted from motor stator current and vibration signals. Neural network is trained and Hosted by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electric and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real time application.

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참고문헌

  1. Electric Power Systems Research v.64 Induction Machine Drive Condition Monitoring and Diagnostic Research- A Survey Singh, G.K.;Kazzaz, S.A.S.A. https://doi.org/10.1016/S0378-7796(02)00172-4
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피인용 문헌

  1. Neural-network-based Fault Detection and Diagnosis Method Using EIV(errors-in variables) vol.21, pp.11, 2011, https://doi.org/10.5050/KSNVE.2011.21.11.1020