Reliability analysis of the components frequently starts with the data that manufacturer provides. If enough failure data are collected from the field operations, the reliability should be recomputed and updated on the basis of the field failure data. However, when the failure time record for a component contains only a few observations, all statistical methodologies are limited. In this case, where the failure records for multiple number of identical components are available, a valid alternative is combining all the data from each component into one data set with enough sample size and utilizing the useful information in the censored data. The ROK Navy has been operating multiple Patrol Killer Guided missiles (PKGs) for several years. The Korea Multi-Function Control Console (KMFCC) is one of key components in PKG combat system. The maintenance record for the KMFCC contains less than ten failure observations and a censored datum. This paper proposes a Bayesian approach with a Dirichlet mixture model to estimate failure time density for KMFCC. Trends test for each component record indicated that null hypothesis, that failure occurrence is renewal process, is not rejected. Since the KMFCCs have been functioning under different operating environment, the failure time distribution may be a composition of a number of unknown distributions, i.e. a mixture distribution, rather than a single distribution. The Dirichlet mixture model was coded as probabilistic programming in Python using PyMC3. Then Markov Chain Monte Carlo (MCMC) sampling technique employed in PyMC3 probabilistically estimated the parameters’ posterior distribution through the Dirichlet mixture model. The simulation results revealed that the mixture models provide superior fits to the combined data set over single models.

1. 서 론
2. 추세 검정
    2.1 완전 자료에 대한 추세 검정
    2.2 중도절단 자료가 포함된 경우의 추세
    2.3 자료 수가 적은 경우의 추세 검정
3. 디리슈레 모형
    3.1 혼합모형
    3.2 디리슈레 혼합모형
    3.3 최적 모형 선택
4. 사례연구
    4.1 추세 검정
    4.2 확률밀도함수 도출
5. 결 론
References

• Jinwhan Lee(한남대학교 산업공학과) | 이진환
• Jung Hun Kim(합동군사대학교 공군대학) | 김정훈
• BongJoo Jung(한남대학교 산업공학과) | 정봉주
• Kyeongtaek Kim(한남대학교 산업공학과) | 김경택 Corresponding Author