Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Diagnosis Method for Stator-Faults in Induction Motor using Park's Vector Pattern and Convolution Neural Network

Park's Vector 패턴과 CNN을 이용한 유도전동기 고정자 고장진단방법

  • Goh, Yeong-Jin (Dept. of Electrical Engineering, Tongmyong University) ;
  • Kim, Gwi-Nam (Dept. of Mechanical and Automotive Engineering, Suncheon Jeil College) ;
  • Kim, YongHyeon (Dept. of Electrical and Semiconductor Engineering, Chonnam National University) ;
  • Lee, Buhm (Dept. of Electrical and Semiconductor Engineering, Chonnam National University) ;
  • Kim, Kyoung-Min (Dept. of Electrical and Semiconductor Engineering, Chonnam National University)
  • Received : 2020.09.14
  • Accepted : 2020.09.25
  • Published : 2020.09.30
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Abstract

In this paper, we propose a method to use PV(Park's Vector) pattern for inductive motor stator fault diagnosis using CNN(Convolution Neural Network). The conventional CNN based fault diagnosis method was performed by imaging three-phase currents, but this method was troublesome to perform normalization by artificially setting the starting point and phase of current. However, when using PV pattern, the problem of normalization could be solved because the 3-phase current shows a certain circular pattern. In addition, the proposed method is proved to be superior in the accuracy of CNN by 18.18[%] compared to the previous current data image due to the autonomic normalization.

본 논문에서는 CNN(Convolution Neural Network)을 이용한 유도전동기 고정자 고장진단에 PV(Park's Vector)패턴을 특징으로 활용하는 방법을 제안하였다. 기존의 CNN을 이용한 유도전동기 고장진단 방법은 3상 전류를 이미지화하여 진단을 수행하였으나, 이 방법은 인위적으로 전류의 시작점, 위상 등을 맞춰 정규화를 수행해야하는 번거러움이 존재하나, PV패턴을 이용할 경우 일정 원의 패턴을 나타내기 때문에 정규화의 문제를 해결 할 수 있었다. 또한 PV패턴을 이용할 경우, 특징벡터가 자동적으로 정규화됨에 따라 기존의 전류데이터를 이미지화한 결과보다 CNN의 정확도 측면에서 18.18[%] 우수함을 실험을 통해 확인할 수 있었다.

Keywords

References

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