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

The Korean Society for Noise and Vibration Engineering (한국소음진동공학회)
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Fault Diagnostics Algorithm of Rotating Machinery Using ART-Kohonen Neural Network

  • 안경룡 (부경대학교 대학원) ;
  • 한천 (부경대학교 대학원) ;
  • 양보석 (부경대학교 기계공학부) ;
  • 전재진 (국방과학연구소 제2체계개발본부) ;
  • 김원철 (경상대학교 기계항공공학부)
  • Published : 2002.10.01
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Abstract

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

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References

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