디리슈레 혼합모형을 이용한 함정 전투체계 부품의 고장시간 분포 추정

• 영문명: An Application of Dirichlet Mixture Model for Failure Time Density Estimation to Components of Naval Combat System
• 발행기관: 한국산업경영시스템학회
• 저자명: 이진환(Jinwhan Lee) 김정훈(Jung Hun Kim) 정봉주(BongJoo Jung) 김경택(Kyeongtaek Kim)
• 간행물 정보: 『산업경영시스템학회지』제42권 제4호, 194~202쪽, 전체 9쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2019.12.30

영문 초록

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.

목차

1. 서 론

2. 추세 검정

3. 디리슈레 모형

4. 사례연구

5. 결 론

키워드

Bayesian Inference Dirichlet Mixture Model Density Estimation