대한기계학회논문집A (Transactions of the Korean Society of Mechanical Engineers A)

  • 제39권2호
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  • Pages.163-168
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  • 2015
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  • 1226-4873(pISSN)
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  • 2288-5226(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
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정확한 신뢰성 해석을 위한 아카이케 정보척도 기반 일반화파레토 분포의 임계점 추정

Threshold Estimation of Generalized Pareto Distribution Based on Akaike Information Criterion for Accurate Reliability Analysis

  • 강승훈 (한양대학교 대학원 자동차공학과) ;
  • 임우철 (한양대학교 대학원 자동차공학과) ;
  • 조수길 (한양대학교 대학원 자동차공학과) ;
  • 박상현 (한양대학교 대학원 자동차공학과) ;
  • 이민욱 (선박해양플랜트연구소 해양플랜트산업기술센터) ;
  • 최종수 (선박해양플랜트연구소 해양플랜트연구부) ;
  • 홍섭 (선박해양플랜트연구소 해양플랜트산업기술센터) ;
  • 이태희 (한양대학교 대학원 자동차공학과)
  • Kang, Seunghoon (Dept. of Automotive Engineering, Graduate School, Hanyang Univ.) ;
  • Lim, Woochul (Dept. of Automotive Engineering, Graduate School, Hanyang Univ.) ;
  • Cho, Su-Gil (Dept. of Automotive Engineering, Graduate School, Hanyang Univ.) ;
  • Park, Sanghyun (Dept. of Automotive Engineering, Graduate School, Hanyang Univ.) ;
  • Lee, Minuk (Technology Center for Offshore Plant Industries, Korea Research Institute of Ships and Ocean Engineering) ;
  • Choi, Jong-Su (Offshore Plant Research Division, Korea Research Institute of Ships and Ocean Engineering) ;
  • Hong, Sup (Technology Center for Offshore Plant Industries, Korea Research Institute of Ships and Ocean Engineering) ;
  • Lee, Tae Hee (Dept. of Automotive Engineering, Graduate School, Hanyang Univ.)
  • 투고 : 2014.09.04
  • 심사 : 2014.10.30
  • 발행 : 2015.02.01
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초록

공학분야의 신뢰성 해석은 점점 더 높은 신뢰도 영역에 대한 확률밀도함수의 예측을 요구한다. 따라서 높은 신뢰도를 정확하게 해석하기 위해 분포의 꼬리부분을 정확하게 표현해야 한다. 최근 들어 꼬리부분에 대한 표본만을 이용해 꼬리 모형을 생성하여 신뢰도를 추정할 수 있는 방법인 일반화파레토 분포에 대한 연구가 활발히 진행되고 있다. 하지만 기존의 연구에서는 부정확한 임계점 추정으로 꼬리부분에서 신뢰도의 정확도가 떨어진다. 따라서 본 논문에서는 아카이케 정보척도를 이용하여 임계점을 정확하고 강건하게 추정하고 이를 통해 꼬리 모형의 정확도를 향상시키는 아카이케 정보척도 기반 일반 화파레토 분포 기법을 제안한다. 또한 제안하는 기법을 이용한 신뢰성 해석을 수행하여 정확도가 향상된 신뢰성 해석 결과를 도출하였다.

In order to perform estimations with high reliability, it is necessary to deal with the tail part of the cumulative distribution function (CDF) in greater detail compared to an overall CDF. The use of a generalized Pareto distribution (GPD) to model the tail part of a CDF is receiving more research attention with the goal of performing estimations with high reliability. Current studies on GPDs focus on ways to determine the appropriate number of sample points and their parameters. However, even if a proper estimation is made, it can be inaccurate as a result of an incorrect threshold value. Therefore, in this paper, a GPD based on the Akaike information criterion (AIC) is proposed to improve the accuracy of the tail model. The proposed method determines an accurate threshold value using the AIC with the overall samples before estimating the GPD over the threshold. To validate the accuracy of the method, its reliability is compared with that obtained using a general GPD model with an empirical CDF.

키워드

참고문헌

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