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

The Korean Society for Noise and Vibration Engineering (한국소음진동공학회)
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Feature Vector Decision Method of Various Fault Signals for Neural-network-based Fault Diagnosis System

신경회로망 기반 고장 진단 시스템을 위한 고장 신호별 특징 벡터 결정 방법

  • 한형섭 (울산대학교 대학원 컴퓨터정보통신공학과) ;
  • 조상진 (울산대학교 전기전자정보시스템공학부) ;
  • 정의필 (울산대학교 컴퓨터정보통신공학부)
  • Received : 2010.06.10
  • Accepted : 2010.10.19
  • Published : 2010.11.20
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Abstract

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying various techniques such as signal processing and pattern recognition. Recently, fault diagnosis systems using artificial neural network have been proposed. For effective fault diagnosis, this paper used MLP(multi-layer perceptron) network which is widely used in pattern classification. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes the decision method of the proper feature vectors about each fault signal for neural-network-based fault diagnosis system. We applied LPC coefficients, maximum magnitudes of each spectral section in FFT and RMS(root mean square) and variance of wavelet coefficients as feature vectors and selected appropriate feature vectors as comparing error ratios of fault diagnosis for sound, vibration and current fault signals. From experiment results, LPC coefficients and maximum magnitudes of each spectral section showed 100 % diagnosis ratios for each fault and the method using wavelet coefficients had noise-robust characteristic.

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References

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