Transactions of the Korean Society of Mechanical Engineers B (대한기계학회논문집B)

The Korean Society of Mechanical Engineers (대한기계학회)
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Fault Severity Diagnosis of Ball Bearing by Support Vector Machine

서포트 벡터 머신을 이용한 볼 베어링의 결함 정도 진단

  • Kim, Yang-Seok (Central Research Institute, Korea Hydro & Nuclear Co., Ltd.) ;
  • Lee, Do-Hwan (Central Research Institute, Korea Hydro & Nuclear Co., Ltd.) ;
  • Kim, Dae-Woong (Central Research Institute, Korea Hydro & Nuclear Co., Ltd.)
  • 김양석 (한국수력원자력(주) 중앙연구원) ;
  • 이도환 (한국수력원자력(주) 중앙연구원) ;
  • 김대웅 (한국수력원자력(주) 중앙연구원)
  • Received : 2012.06.14
  • Accepted : 2013.03.19
  • Published : 2013.06.01
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Abstract

A support vector machine (SVM) is a very powerful classification algorithm when a set of training data, each marked as belonging to one of several categories, is given. Therefore, SVM techniques have been used as one of the diagnostic tools in machine learning as well as in pattern recognition. In this paper, we present the results of classifying ball bearing fault types and severities using SVM with an optimized feature set based on the minimum distance rule. A feature set as an input for SVM includes twelve time-domain and nine frequency-domain features that are extracted from the measured vibration signals and their decomposed details and approximations with discrete wavelet transform. The vibration signals were obtained from a test rig to simulate various bearing fault conditions.

서포트 벡터 머신(Support Vector Machine, SVM)은 학습용 데이터 집합이 확보되어 있을 경우, 매우 강력한 분류 알고리즘이다. 따라서 패턴인식은 물론 기계학습 분야에서 결함진단 도구의 하나로 이용되고 있다. 본 논문에서는 최적 특징과 SVM 을 이용하여 볼 베어링의 결함유형과 결함의 정도를 진단한 결과를 기술하였다. SVM 학습용 특징데이터에는 12 개의 시간영역 특징과 9 개의 주파수영역 특징들이 포함되어 있으며 이들 특징들은 다양한 베어링 결함조건에서 측정된 진동신호와 진동신호의 이산 웨이블렛 변환신호로부터 추출되었다.

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References

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