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결함유형별 최적 특징과 Support Vector Machine 을 이용한 회전기계 결함 분류

Fault Classification for Rotating Machinery Using Support Vector Machines with Optimal Features Corresponding to Each Fault Type

  • 투고 : 2010.06.07
  • 심사 : 2010.08.27
  • 발행 : 2010.11.01

초록

Support Vector Machine(SVM)을 이용한 회전기계 진단 연구가 많이 수행되어 왔으나 결함 분류성능은 입력 특징과 더불어 다중 분류 방법, 이진분류기, 커널함수 등에 따라 다르다. SVM 을 이용한 대부분의 기존 연구들은 한번 입력 특징들을 선정하면 결함 분류시 동일한 특징데이터를 이용한다. 본 논문에서는 회전기계의 다양한 결함조건에서 측정한 진동신호로부터 추출한 통계적 특징들을 이용하여 각각의 결함을 분류하기 위한 최적 특징들을 선정한 후, 해당 결함상태를 분류하기 위한 SVM 학습과 분류에 각각 이용하였다. 실험자료를 이용한 검증 결과, 제안한 단계 분류 방법이 상대적으로 적은 학습시간으로 단일 다중 분류 방법과 유사한 분류 성능을 얻을 수 있었다.

키워드

결함분류;특징선택;회전기계

참고문헌

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피인용 문헌

  1. Prognostic Technique for Ball Bearing Damage vol.37, pp.11, 2013, https://doi.org/10.3795/KSME-A.2013.37.11.1315