Journal of KSNVE (소음진동)

The Korean Society for Noise and Vibration Engineering (한국소음진동공학회)
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Abnormal Vibration Diagnostics Algorithm of Rotating Machinery Using Self-Organizing Feature Map nad Learing Vector Quantization

자기조직화특징지도와 학습벡터양자화를 이용한 회전기계의 이상진동진단 알고리듬

  • Published : 2000.04.01
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Abstract

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

Keywords

References

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