• International Journal of Reliability and Applications
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• 제12권1호
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• pp.41-48
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• 2011

This paper considers the maintenance model suggested by Jung and Park (2010) to adopt the Bayesian approach and obtain an optimal replacement policy following the expiration of NFRRW. As the criteria to determine the optimal maintenance period, we use the expected cost during the life cycle of the system. When the failure times are assumed to follow a Weibull distribution with unknown parameters, we propose an optimal maintenance policy based on the Bayesian approach. Also, we describe the revision of uncertainty about parameters in the light of data observed. Some numerical examples are presented for illustrative purpose.

• 한국신뢰성학회지:신뢰성응용연구
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• 제13권4호
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• pp.229-239
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• 2013

This paper reports a manner to use a Bayesian approach to derive the optimal replacement policy. In order to produce a system with minimal repair warranty, a replacement model with the extended warranty is considered. Within the warranty period, the failed system is minimally repaired by the manufacturer at no cost to the end-user. The failure time is assumed to follow a Weibull distribution with unknown parameters. The expected cost rate per unit time, from the end-user's viewpoints, is induced by the Bayesian approach, and the optimal replacement policy to minimize the cost rate is proposed. Finally, a numerical example illustrating to derive the optimal replacement policy based on the Bayesian approach is described.

• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.743-752
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• 2006

This paper considers a Bayesian approach to replacement policy model with minimal repair. We use the criterion based on the expected cost and the expected downtime to determine the optimal replacement period. To do so, we obtain the expected cost rate per unit time and the expected downtime per unit time, respectively. 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 maintenance policy. Especially, the overall value function suggested by Jiagn and Ji(2002) is applied to obtain the optimal replacement period. The numerical examples are presented for illustrative purpose.

• 한국환경과학회지
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• 제27권7호
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• pp.499-507
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• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• 품질경영학회지
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• 제52권1호
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• pp.43-56
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• 2024

Purpose: The injection molding process, crucial for plastic shaping, encounters difficulties in sustaining product quality when replacing injection machines. Variations in machine types and outputs between different production lines or factories increase the risk of quality deterioration. In response, the study aims to develop a system that optimally adjusts conditions during the replacement of injection machines linked to molds. Methods: Utilizing a dataset of 12 injection process variables and 52 corresponding sensor variables, a predictive model is crafted using Decision Tree, Random Forest, and XGBoost. Model evaluation is conducted using an 80% training data and a 20% test data split. The dependent variable, classified into five characteristics based on temperature and pressure, guides the prediction model. Bayesian optimization, integrated into the selected model, determines optimal values for process variables during the replacement of injection machines. The iterative convergence of sensor prediction values to the optimum range is visually confirmed, aligning them with the target range. Experimental results validate the proposed approach. Results: Post-experiment analysis indicates the superiority of the XGBoost model across all five characteristics, achieving a combined high performance of 0.81 and a Mean Absolute Error (MAE) of 0.77. The study introduces a method for optimizing initial conditions in the injection process during machine replacement, utilizing Bayesian optimization. This streamlined approach reduces both time and costs, thereby enhancing process efficiency. Conclusion: This research contributes practical insights to the optimization literature, offering valuable guidance for industries seeking streamlined and cost-effective methods for machine replacement in injection molding.

• Nuclear Engineering and Technology
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• 제54권9호
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• pp.3225-3234
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• 2022

Alloy 690 tubing has been shown to be highly resistant to primary water stress corrosion cracking (PWSCC). Nevertheless, predicting the failure by PWSCC in Alloy 690 SG tubes is indispensable. In this work, a Bayesian-based statistical approach is proposed to predict the occurrence of failure by PWSCC in Alloy 690 SG tubing. The prior distributions of the model parameters are developed based on the prior knowledge or information regarding the parameters. Since Alloy 690 is a replacement for Alloy 600, the parameter distributions of Alloy 600 tubing are used to gain prior information about the parameters of Alloy 690 tubing. In addition to estimating the model parameters, analysis of tubing reliability is also performed. Since no PWSCC has been observed in Alloy 690 tubing, only right-censored free-failure life of the tubing are available. Apparently the inference is sensitive to the choice of prior distribution when only right-censored data exist. Thus, one must be careful in choosing the prior distributions for the model parameters. It is found that the use of non-informative prior distribution yields unsatisfactory results, and strongly informative prior distribution will greatly influence the inference, especially when it is considerably optimistic relative to the observed data.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.313-335
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• 1995

Suppose that we have $k(k \geq 2)$ populations (or systems), say $\pi_1, \cdots, \pi_k$, to be tested. Under the type-II censored testing without replacement we consider the problem of estimating the unknown parameters of interests and the reliability for a given time t for each population. Also we compare the perfomances of the proposed Bayes estimators with another estiamtors under the Jeffrey-type noninformative prior distribution.

• 응용통계연구
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• 제31권6호
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• pp.677-692
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• 2018

최근 2017년 우리나라 합계출산율은 1.05명로 2005년 1.08명 수준으로 회귀하는 현상을 보이고 있다. 1.05명은 인구대체선(2.1명), 안전선(1.5명)과도 거리가 먼 초저출산 수준이고 마치 초저출산 덫에 빠질 우려가 있다. 이에 합계출산율의 합리적인 예측과 이를 통한 출산정책에 유용한 자료를 제공하는 것은 그 어느 때 보다도 중요하다. 그 동안 다양한 통계적 방법으로 합계출산율 추이를 예측하였는데, 데이터 완비성이 높고 품질이 좋은 경우 모형 접근인 모수적 방법, 데이터 추이가 단절되거나 변동이 심한 경우 평활과 가중치를 적용한 비모수적 방법, 데이터 부족과 품질 등으로 선진국의 출산율 3단계 전이현상을 참고하여 이들의 사전분포를 활용하는 베이지안 방법 등이 적용되어 왔다. 본 연구는 최근 변동이 심한 우리나라 출산율에 모수, 비모수, 그리고 베이지안 방법을 적용하여 추정과 예측을 실시하고 도출된 결과 비교를 통해 적합성과 타당성 측면에서 어떤 방법이 합리적인지 모색하고자 한다. 분석결과 합계출산율 예측값 순위는 통계청 합계출산율이 가장 높고, 베이지안, 모수, 비모수 순으로 나타났다. 2017년 TFR 1.05명 수준을 감안할 때 모수, 비모수모형으로 도출된 합계출산율 예측값이 합리적이다. 또한 출산율 자료완비성이 높고 품질이 우수할 경우 계산 효율성과 적합도 관점에서 모수적 추정과 예측 접근 방법이 타 방법보다 우수한 것으로 도출되었다.

• Journal of the Korean Data and Information Science Society
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• 제4권
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• pp.65-74
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• 1993

Suppose that we have two populations(or systems), say ${\Pi}_{1}\;and\;{\Pi}_{2}$, to be tested. A random sample of size n from each population is taken and the test for each system will be terminated when the first r failures among n random samples are observed. This kind of test is caned the type-II censored (or item-censored) testing without replacement. Under this scheme we consider the problem of estimating the unknown parameters of interests and the reliability for a given time t for each population.