Comparative Study of Reliability Analysis Methods for Discrete Bimodal Information

바이모달 이산정보에 대한 신뢰성해석 기법 비교

  • Lim, Woochul (Dept. of Automotive Engineering, College of Engineering, Hanyang Univ.) ;
  • Jang, Junyong (Dept. of Automotive Engineering, College of Engineering, Hanyang Univ.) ;
  • Lee, Tae Hee (Dept. of Automotive Engineering, College of Engineering, Hanyang Univ.)
  • 임우철 (한양대학교 공과대학 미래자동차공학과) ;
  • 장준용 (한양대학교 공과대학 미래자동차공학과) ;
  • 이태희 (한양대학교 공과대학 미래자동차공학과)
  • Received : 2012.12.28
  • Accepted : 2013.05.29
  • Published : 2013.07.01


The distribution of a response usually depends on the distribution of a variable. When the distribution of a variable has two different modes, the response also follows a distribution with two different modes. In most reliability analysis methods, the number of modes is irrelevant, but not the type of distribution. However, in actual problems, because information is often provided with two or more modes, it is important to estimate the distributions with two or more modes. Recently, some reliability analysis methods have been suggested for bimodal distributions. In this paper, we review some methods such as the Akaike information criterion (AIC) and maximum entropy principle (MEP) and compare them with the Monte Carlo simulation (MCS) using mathematical examples with two different modes.


Reliability Analysis;Akaike Information Criterion(AIC);Finite Mixture Model(FMM);Maximum Entropy Principle;Monte Carlo Simulation(MCS);Bimodal Distribution


Supported by : 한국연구재단


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