• 산업경영시스템학회지
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• 제40권4호
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• pp.96-104
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• 2017

A one-shot system (device) refers to a system that is stored for a long period of time and is then disposed of after a single mission because it is accompanied by a chemical reaction or physical destruction when it operates, such as shells, munitions in a defense weapon system and automobile airbags. Because these systems are primarily related with safety and life, it is required to maintain a high level of storage reliability. Storage reliability is the probability that the system will operate at a particular point in time after storage. Since the stored one-shot system can be confirmed only through inspection, periodic inspection and maintenance should be performed to maintain a high level of storage reliability. Since the one-shot system is characterized by a large loss in the event of a failure, it is necessary to determine an appropriate inspection period to maintain the storage reliability above the reliability goal. In this study, we propose an optimal inspection policy that minimizes the total cost while exceeding the reliability goal that the storage reliability is set in advance for the one-shot system in which periodic inspections are performed. We assume that the failure time is the Weibull distribution. And the cost model is presented considering the existing storage reliability model by Martinez and Kim et al. The cost components to be included in the cost model are the cost of inspection $c_1$, the cost of loss per unit time between failure and detection $c_2$, the cost of minimum repair of the detected breakdown of units $c_3$, and the overhaul cost $c_4$ of $R_s{\leq}R_g$. And in this paper, we will determine the optimal inspection policy to find the inspection period and number of tests that minimize the expected cost per unit time from the finite lifetime to the overhaul. Compare them through numerical examples.

• 한국정보전자통신기술학회논문지
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• 제9권4호
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• pp.351-358
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• 2016

과학기술이 급속하게 발전함에 따라 더 강력한 소프트웨어 기능의 급속한 발전과 함께 소프트웨어의 복잡성이 크게 증가함으로써 소프트웨어 테스트 및 신뢰성 평가의 어려움이 증가하고 있다. 소프트웨어 고장분석을 위한 비동질적인 포아송 과정에서 결함당 고장발생률이 상수이거나, 단조 증가 또는, 단조 감소하는 패턴을 가질 수 있다. 본 논문에서는 결함의 기대값을 가정하는 유한고장 소프트웨어 NHPP 모형과 수리시점에서도 고장이 발생할 상황을 반영하는 무한고장 NHPP 모형들을 상호 비교 제시하였다. 소프트웨어 신뢰성 분야에서 많이 사용되는 어랑분포에 근거한 유한고장과 무한고장 소프트웨어 신뢰성 모형에 대한 신뢰도 성능을 비교 분석하였다. 그 결과 유한고장 모형이 무한고장 모형보다 효율적으로 좋게 나타났으며, 이 과정에서 모수추정법은 최우추정법을 이용하였다. 본 연구결과를 통하여 소프트웨어 개발자들에게 소프트웨어 고장현상을 파악하는데 도움을 줄 수 있을 것으로 판단된다.

• 한국정보전자통신기술학회논문지
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• 제8권3호
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• pp.195-202
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• 2015

소프트웨어 고장분석을 위한 비동질적인 포아송과정에서 결함당 고장발생률이 상수이거나, 단조 증가 또는 단조 감소하는 패턴을 가질 수 있다. 본 논문에서는 결함의 기대값을 가정하는 유한고장소프트웨어 NHPP모형과 수리시점에서도 고장이 발생할 상황을 반영하는 무한고장 NHPP모형들을 비교 제시하였다. 소프트웨어 신뢰성분야에서 많이 사용되는 지수파우어분포에 근거한 유한고장과 무한고장 소프트웨어 신뢰성모형에 대한 비교문제를 제시하였다. 그 결과 유한고장모형이 무한고장모형보다 효율적으로 나타났다. 그리고 모수추정법은 최우추정법을 이용하였다. 이 연구를 통하여 소프트웨어 개발자들은 소프트웨어 고장현상을 파악하는데 어느 정도 도움을 줄 수 있을 것으로 사료된다.

• 한국신뢰성학회지:신뢰성응용연구
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• 제7권1호
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• pp.13-22
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• 2007

A preventive maintenance model, caller FNBM (${\alpha},{\delta},{\gamma}$) model, is proposed to decide an optimal repair number under achieved availability requirements (r) along with taking two types of failures (repairable or irrepairable) into account. In this model, the current system is replaced by a new one in case when it doesn't meet the achieved availability requirement, even though it is repairable failure; Othewise it is replaced in time of the first irrepairable failure. Assumed that the j-th failure is repairable with probability ${\alpha}_j$ minimal repairs are allowed for repairable failure between replacements. Expected cost rate for preventive maintenance model is developed using NHPP (Non - Homogeneous Poisson Process) in order to de term in the optimal number $n^*$, also numerical examples are shown in order to explain the proposed model. Since the proposed FNBM (${\alpha},{\delta},{\gamma}$) model includes Park FNBM model (1979) and Nakagawa FNBM (p) model (1983) m this proposed model is thought to be better than previous model, especially for weapon system which requires availability as primary parameter.

• Journal of the Korean Data and Information Science Society
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• 제21권5호
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• pp.873-882
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• 2010

본 논문에서는 비재생혼합보증이 종료된 이후의 교체정책에 대한 베이지안 접근을 고려한다. 이때, 비재생혼합보증은 비재생무료교체보증과 비재생비례교체보증의 혼합된 형태가 된다. 최적의 교체주기를 결정하기 위하여 기대비용과 기대비가동시간에 근거한 기준이 사용되는데, 이를 위해서 단위시간당 기대비용과 단위시간당 기대비가동시간이 각각 구해진다. 시스템의 고장시간이 불확실한 모수를 갖는 와이블분포를 할 때, 베이지안 접근에 근거하여 최적의 교체정책이 제안된다. 이때, 최적의 교체주기를 결정하기 위해서 Jiang과 Ji (2002)에 의해서 제안된 총밸류함수가 사용된다. 끝으로, 본 논문에서 제안된 베이지안 교체정책을 설명하기 위해서 수치적 예를 살펴본다.

• 디지털융복합연구
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• 제12권12호
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• pp.277-284
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• 2014

소프트웨어 고장분석을 위한 비동질적인 포아송과정에서 결함당 고장발생률이 상수이거나, 단조 증가 또는 단조 감소하는 패턴을 가질 수 있다. 본 논문에서는 결함의 기대값을 가정하는 유한고장소프트웨어 NHPP모형과 수리시점에서도 고장이 발생할 상황을 반영하는 무한고장 NHPP모형들을 비교 제시하였다. 소프트웨어 신뢰성분야에서 많이 인용되는 레일리분포를 이용한 유한고장과 무한고장 소프트웨어 신뢰성모형에 대한 비교문제를 탐색한 결과 무한고장모형이 유한고장모형보다 효율적으로 나타났다. 이러한 비교문제를 위하여 모수추정은 최우추정법을 이용하였다. 이 연구를 통하여 소프트웨어 개발자에게 소프트웨어 고장현상을 파악하는데 어느 정도 도움을 줄 수 있을 것으로 사료 된다.

• 한국신뢰성학회지:신뢰성응용연구
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• 제16권2호
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• pp.162-170
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• 2016

Purpose: The purpose of this study was to analyze the failure modes of an auto transfer switch (ATS), determine the most common failure mechanisms, and iterate the design to improve reliability. Methods: We carried out failure mode and effect analysis (FMEA) to determine the failure modes and mechanisms. We identified the parts or modules that required improvement via two-stage quality function deployment based on FMEA, and improvements to reliability were monitored using the Gomperz growth model. Results: The main failure modes of the ATS were damage to, and deformation of, the stator / movable element due to repetitive movements. Five iterations of design modification were carried out, and the mean time to failure (MTTF) increased to 14,539 cycles, corresponding to 85% of the target MTTF. The Gompertz growth model indicates that the 10th iteration of design modification is expected to achieve the target MTTF. Conclusion: We improved the reliability of mechanical parts via failure mode analysis, and characterized the iterative improvements in the MTTF using the Gompertz growth model.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.689-696
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• 2007

This paper considers a Bayesian approach to determine an optimal PM policy with random maintenance quality. Thus, we assume that the quality of a PM action is a random variable following a probability distribution. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal PM policy. Finally, the numerical examples are presented for illustrative purpose.

• Industrial Engineering and Management Systems
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• 제12권1호
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• pp.41-45
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• 2013

Age replacement policy is a commonly policy in maintenance management of spare part. It means that a spare part is always replaced at failure or fixed time after its installation, whichever occurs first. An optimal age replacement policy of spare parts concerns with finding the optimal replacement time determined by minimizing the expected cost per unit time. The age of the part was generally assumed to be a random variable in the past literatures, but in many situations, there are few or even no observed data to estimate the probability distribution of part's lifetime. In order to solve this phenomenon, a new uncertain age replacement policy has been proposed recently, in which the age of the part was assumed to be an uncertain variable. This paper discusses the optimal age replacement policies by dealing with the parts' lifetimes as different distributed uncertain variables. Several results on the optimal age replacement time are provided when the lifetimes are described by the uncertain linear, zigzag and lognormal distributions.

• Journal of information and communication convergence engineering
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• 제17권2호
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.