• Journal of Korean Society of Steel Construction
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• v.14 no.2
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• pp.365-373
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• 2002

In this paper, the system reliability concept was presented to predict the lifespan of bridges. Lifetime distribution functions (survivor functions) were used to model real bridges to predict their remaining life. Using the system reliability concept and lifetime distribution functions (survivor functions), a program called LIFETIME was developed. The survivor functions give the reliability of component at time t. The program was applied to an existing Colorado state highway bridge to predict the failure probability of the time-dependent system. The bridge was modeled as a system, with failure probability computed using time-dependent deteriorating models.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1151-1156
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• 2023

In this paper, we propose a deep learning model that utilizes charge/discharge data from initial lithium-ion batteries to predict the remaining useful life of lithium-ion batteries. We build the DMP using the PNP model. To demonstrate the performance of DMP, we organize DML using the LSTM model and compare the remaining useful life prediction performance of lithium-ion batteries between DMP and DML. We utilize the RMSE and RMSPE error measurement methods to evaluate the performance of DMP and DML models using test data. The results reveal that the RMSE difference between DMP and DML is 144.62 [Cycle], and the RMSPE difference is 3.37 [%]. These results indicate that the DMP model has a lower error rate than DML. Based on the results of our analysis, we have showcased the superior performance of DMP over DML. This demonstrates that in the field of lithium-ion batteries, the PNP model outperforms the LSTM model.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.4
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• pp.191-198
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• 1999

수많은 판형부재로 구성된 대형 트러스교에서는 불량용접된 부재가 존재할 수 있으며, 이는 교량의 피로수명에 결정적인 영향을 줄 수 있다. 이러한 교량에서 용접불량부를 조사하고 부재단면중 용접불량을 고려한 유효단면을 가정하여 피로수명을 예측하는 방법에 대하여 연구하였다. 이를 위하여 피로수명에 영향을 미치는 교통량을 분석하고 차량모델을 가정하여 유효등가응력을 산정하였으며 모형피로시험에서 구한 응력-반복횟수곡선을 이용하여 피로수명을 예측하였다. 본 연구에서 사용된 분석기법은 불량용접부와 교통량이 강교량의 피로수명에 미치는 영향을 예측하는데 매우 유용함을 알 수 있다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.417-428
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• 2021

Since future maintenance cost estimation of infrastructure involves uncertainty, it is important to make use of a failure prediction model. However, it is difficult for local governments to develop accurate failure prediction models applicable to infrastructure due to a lack of budget and expertise. Therefore, this study estimated the life expectancy of asphalt road pavement of national highways using the Bayesian Markov Mixture Hazard model. In addition, in order to accurately estimate life expectancy, environmental variables such as traffic volume, ESAL (Equivalent Single Axle Loads), SNP (Structural Number of Pavement), meteorological conditions, and de-icing material usage were applied to retain reliability of the estimation results. As a result, life expectancy was estimated from at least 13.09 to 19.61 years by region. By using this approach, it is expected that it will be possible to estimate future maintenance cost considering local failure characteristics.

• Journal of the Korea Concrete Institute
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• v.15 no.4
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• pp.529-540
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• 2003

Recently, the researches on the durability design of concrete structures have been studied. As the examples, models to evaluate the service life prediction of the structure have been developed. The purpose of this article is to develop the model for predicting remaining service life. The final aim is to provide the user time for repairing the concrete structures. In addition, it makes possible to maintain the concrete structure economically. 70 reservoirs out of the inland concrete structures were selected and concrete structures of their components were surveyed. Two methods were used for measuring carbonation; TG/DTA method and Phenolphtalein indicator and, the value of pH was measured by the pH meter, After deriving correlations of calcium carbonate and used year, duration from completion year to 2002, pH value, and concrete cover depth the model was developed for predicting remaining service life by measuring data as small as possible. The conventional models had been developed on the basis of experiment data obtained from the restricted lab environment like as carbon gas exposure. On the other hand this model was developed on the basis of measuring data obtained from the real field that the complex deterioration actions are occurred such as freezing and thawing, carbonation, steel corrosion, and so on. The reliability of the developed model will be evaluated high in this point and this model can help to maintain concrete structures economically by providing the manager time to repair the deteriorated concrete structures in site of facility management.

• Journal of Energy Engineering
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• v.22 no.4
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• pp.338-346
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• 2013

The conventional lead acid battery is optimized for cranking performance of engine. Recently electric devices and fuel economy technologies of battery have influenced more deep cycle of dynamic behavior of battery. I also causes to reduce battery life-time. This study proposed that aging battery model is focused for increasing of battery durability. The stress factors of battery aging consist of discharge rate, charging time, full charging time and temperature. This paper considers the electrochemical kinetics, the ionic species conservation, and electrode porosity. For prediction of battery life cycle we consider battery model containing strong impacts, corrosion of positive grid and shedding. Finally, we validated that modeling results were compared with the accelerated thermal measurement data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1446-1452
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• 1994

In this paper, Weibull distribution is applied to the lifetme distribution of a device. The method of Bayesian estimate used to estimate requiring parameter in order to predict lifetime of device using accelerated lifetime test data, namely failure time of device. The method of Bayesian estimate needs prior information in order to estimate parameter. But this paper proposed the method of parameter estimate without prior information. As stress is temperature, Arrhenius model is applied and the method of linear estimate is applied to predict lifetime of device at the state of normal operation.

• Proceedings of the KAIS Fall Conference
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• 2007.11a
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• pp.319-322
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• 2007

철도차량의 피로수명예측은 안전성과 신뢰성을 확보하고 높은 품질을 위한 중요한 관점이다. 이것은 최적설계 과정에서 추가의 제한조건으로 최소 피로수명값을 사용해서 접근할 수 있다. 하지만 피로수명은 회수의 함수이기 때문에 최적설계 적용에 제약이 따른다. 본 연구는 피로수명예측을 위한 최적설계에 대해 2단계 반응표면모델의 응용을 제안한다. 적용 예제로 컨테이너 화차의 제동 브라켓 엔드빔의 피로파손 문제에 대해 제안한 방법의 유효성을 설명한다.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.3
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• pp.30-36
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• 2013

This paper presents a method to estimate a storage life for loss of stabilizer content as storage periods using accelerated life test data. The estimate of storage life based on deterministic accelerated life test and degradation data cannot describe a condition distribution and storage life distribution. Previously, the method to show the condition distribution and storage life distribution by using gamma process has been studied. But it has limitation because it is impossible to collect the deterioration data at initial production phase. The estimated storage life presented by this study shows the similar value to previous studies and the method can describe the condition distribution and storage life distribution. So, the estimation method studied in this paper can be used for a life cycle management about deterioration of propellant for propulsion unit or components of missile, too.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.91-97
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• 2020

Lithium-ion batteries (LIBs) have a longer lifespan, higher energy density, and lower self-discharge rates than other batteries, therefore, they are preferred as an Energy Storage System (ESS). However, during years 2017-2019, 28 ESS fire accidents occurred in Korea, and accurate capacity estimation of LIB is essential to ensure safety and reliability during operations. In this study, data-driven modeling that predicts capacity changes according to the charging cycle of LIB was conducted, and developed models were compared their performance for the selection of the optimal machine learning model, which includes the Decision Tree, Ensemble Learning Method, Support Vector Regression, and Gaussian Process Regression (GPR). For model training, lithium battery test data provided by NASA was used, and GPR showed the best prediction performance. Based on this study, we will develop an enhanced LIB capacity prediction and remaining useful life estimation model through additional data training, and improve the performance of anomaly detection and monitoring during operations, enabling safe and stable ESS operations.