Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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High-Reliable Classification of Multiple Induction Motor Faults using Robust Vibration Signatures in Noisy Environments based on a LPC Analysis and an EM Algorithm

LPC 분석 기법 및 EM 알고리즘 기반 잡음 환경에 강인한 진동 특징을 이용한 고 신뢰성 유도 전동기 다중 결함 분류

  • Kang, Myeongsu (School of Electrical, Eletronics and Computer Engineering, University of Ulsan) ;
  • Jang, Won-Chul (School of Electrical, Eletronics and Computer Engineering, University of Ulsan) ;
  • Kim, Jong-Myon (School of Electrical, Eletronics and Computer Engineering, University of Ulsan)
  • 강명수 (울산대학교 전기전자컴퓨터공학과) ;
  • 장원철 (울산대학교 전기전자컴퓨터공학과) ;
  • 김종면 (울산대학교 전기전자컴퓨터공학과)
  • Received : 2013.11.11
  • Accepted : 2014.01.19
  • Published : 2014.02.28
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Abstract

The use of induction motors has been recently increasing in a variety of industrial sites, and they play a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of induction motors in order to reduce economical damage caused by their faults. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results show that the proposed approach yields higher classification accuracies than the state-of-the-art conventional approach for both noiseless and noisy environments for identifying the induction motor faults.

최근 산업 현장에서 유도 전동기의 사용이 증대되고 있으며, 유도 전동기는 산업 현장에서 중요한 역할을 하고 있다. 따라서 유도 전동기의 결함으로 인한 피해를 최소화하기 위해 유도 전동기의 결함 검출 및 분류 시스템의 개발이 중요한 문제로 대두되고 있다. 따라서 본 논문에서는 유도전동기의 결함을 조기에 식별하기 위해 선형예측 코딩(LPC)기법과 Expectation Maximization(EM) 알고리즘을 이용하여 각각의 유도 전동기 고장의 스펙트럼 포락처리 모델을 추정한다. 앞서 두 기법을 사용하여 추정된 고장 유형 모델과 마할라노비스 거리(MD) 기법을 사용하여 유도전동기의 결합을 분류한다. 또한 제안된 알고리즘 성능을 평가하기 위해 기존에 제안된 진동 신호의 특징을 이용한 유도 전동기 결함 분류 알고리즘과 분류 정확도 측면에서 성능을 검증하였다. 실험 결과, 제안하는 알고리즘은 잡음이 없는 환경 및 잡음이 섞인 환경에서도 높은 분류 성능을 보였다.

Keywords

References

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