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

  • 제19권7호
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  • Pages.726-735
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  • 2009
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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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웨이블렛 계수의 분산과 상관도를 이용한 유도전동기의 고장 검출 및 진단

Fault Detection and Diagnosis for Induction Motors Using Variance, Cross-correlation and Wavelets

  • ;
  • 조상진 (울산대학교 전기전자정보시스템공학부) ;
  • 정의필 (울산대학교 컴퓨터정보통신공학부)
  • 발행 : 2009.07.20
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초록

이 논문에서는 신호 모델에 기반하여 유도전동기의 고장 검출 및 고장 진단을 위한 새로운 시스템을 제안한다. 산업현장에 적용하는 기존의 제품들은 신호가 문턱치를 넘어면 고장을 검출하는 단순한 알고리듬을 가지고 있어 고장의 유형이나 고장을 예측하는데 문제가 있다. 이 논문에서는 이러한 문제들을 해결하기 위한 시스템을 제안한다. 이 시스템은 고장 검출 과정과 고장 진단 과정으로 구성되며, 고장 검출 과정은 기계 신호음들이 웨이블렛 필터뱅크를 통과한 후 웨이블렛 계수들의 분산과 상관도를 분석하여 고장을 검출한다. 고장 진단 과정은 패턴분류기술을 적용하여 고장의 유형을 진단하게 된다. 대표적인 유도전동기 고장 유형들로서는 불평형, 미스얼라이먼트, 그리고 베어링 루스 등이 있으며, 이러한 유형들은 제안하는 시스템에서 분석되고 진단을 받게 된다. 제안하는 시스템에 적용한 결과 상관도를 이용한 방법은 78 %, 분산을 이용한 방법은 95 % 이상의 고장진단율을 보이는 우수한 결과를 나타내었다.

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

키워드

참고문헌

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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