• Journal of Aerospace System Engineering
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• v.13 no.6
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• pp.17-25
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• 2019

PHM (Prognostics and Health Management) of aircraft engines is applied to predict the remaining useful life before failure or the lifetime limit. There are two methods to establish a predictive model for this: The physics-based method and the data-driven method. The physics-based method is more accurate and requires less data, but its application is limited because there are few models available. In this study, the data-driven method is applied, in which a multi-layer perceptron based neural network algorithms is applied for the life prediction. The neural network is trained using the data sets virtually made by the C-MAPSS code developed by NASA. After training the model, it is applied to the test data sets, in which the confidence interval of the remaining useful life is predicted and validated by the actual value. The performance of proposed method is compared with previous studies, and the favorable accuracy is found.

• Proceedings of the Korean Reliability Society Conference
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• 2004.07a
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• pp.123-128
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• 2004

본 논문에서는 가속 수명 시험을 통하여 열전소자의 수명 분포, 모수 등을 규명하였으며 고장 분석을 통하여 열전 소자의 수명 증가를 위한 대책 방안을 논의하였다. 가속 수명 시험 결과 열전 소자는 형상 모수 3,6인 Weibull 분포를 따름을 알 수 있었다. 열전 소자가 반도체 부품임에도 불구하고 형상 모수가 큰 이유는 반복 Bending에 의한 피로 파괴가 발생하기 때문임을 고장 분석을 통하여 규명하였다. 위의 고장 메커니즘을 설명할 수 있는 가속 모델식은 Coffin-Manson식으로 설명되어 질 수 있으며 가속수명시험 결과 재료 상수는 1.8임을 알 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.456-458
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• 2022

Due to the environmental regulations of the International Maritime Organization, shipyards are conducting various researches to improve the efficiency of ships, and efforts are being made to reduce the weight of ships. Recently, composite materials including CNT materials have the advantage of being able to reduce weight by 40% or more compared to general steel plate materials, and have the advantage of being able to be used as a substitute for ship clamps or door skins. Therefore, in this study, to predict the life of composite materials including CNT materials, the results were compared through the accelerated deterioration test method and the life prediction using machine learning techniques. The accelerated degradation test used the Arrhenius model equation, and the machine learning method predicted the life using a regression analysis algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.7
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• pp.612-617
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• 2011

An experimental study was carried out for the Elbrodur-NIB(copper alloy) at room temperature under stress-controlled uniaxial fatigue loading with and without mean stress. As a result, the effects of stress amplitude, mean stress and stress rate on ratcheting behavior were investigated. The ratcheting strain increased with increasing stress amplitude for a given mean stress, and with mean stress for a given stress amplitude. But, the ratcheting strain decreased as the stress rate increased. The three mean stress models were investigated and the mean stress models of Smith-Watson-Topper and Walker yielded good correlation of fatigue lives in the life range of $10^2-10^5$cycles.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3633-3642
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• 2015

Single-base propellants contain a single energetic component: nitrocellulose. Accurate predictions of propellant shelf-life should result in cost savings in terms of human and material resources. This study derived an optimized kinetic model reaction order that described stabilizer consumption and estimated propellant shelf-life. High temperature accelerated aging tests gave an optimum reaction order value of 1.15481, from which the minimum standard error of a linear regression estimate of 16.284 was obtained. At normal storage temperature of $21-30^{\circ}C$, propellants should have a safe shelf-life of 140 years, and a minimum of 35 years. It is necessary to consider the temperature range in ammunition storage areas to predict propellant shelf-life more accurately.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.568-574
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• 2021

According to the Framework Act on Sustainable Infrastructure Management, which has been enforced since 2020, reservoirs should be managed to minimize life cycle costs caused by aging through preemptive management such as systematic maintenance and performance improvement. For maintenance in consideration of the life cycle, it is essential to derive the end of life due to continuous performance degradation as the common period increases. For this purpose, it is necessary to develop a performance-predicting model for reservoirs. In this study, a reservoir was divided into main complex facilities to develop a model for the maintenance of the life cycle. A model was developed for each facility. For model development, maintenance information data were collected under management by the Rural Community Corporation. The data available for model development were selected by analyzing the collected data. The developed model was used to predict the expected life expectancy of the reservoir in the current maintenance system and the expected life expectancy in the case of no action. By using the developed model, it is expected that it will be possible to support decision making in operation management and maintenance while considering the life cycle of the reservoir.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.359-360
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• 2020

리튬 이온 배터리가 전기 자동차의 주 동력원으로 사용됨에 따라 배터리의 잔존 수명 예측기술의 중요성이 부각되고 있다. 사용 환경에 적합한 잔존 수명 예측을 위해 전기 자동차의 주행 환경을 모사하여 충전 및 방전이 빈번하게 나타나는 UDDS 프로파일에서 범용적으로 사용할 수 있는 수명 인자를 선정하는 것이 필수적이다. 배터리의 잔존 용량과 가장 상관도가 높은 수명 인자를 선정함으로써, 인공지능 기반 예측 알고리즘의 정확도 향상을 기대 할 수 있으며, 태양광 ESS와 같은 상이한 특성의 어플리케이션에도 범용적인 적용이 가능하다.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.151-159
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• 2008

Performances of retroreflectivity vary place to place, according to traffic volumes and time lengths after striping, depending on pavement marking materials and colors. The present paper uses the nation wide data of retroreflectivity, which has been collected from freeways and then tries to develop the regression curve setting traffic volume and service life as independent variables and retroreflectivities as dependent variables. The DB system includes two year's measurement in $2005{\sim}2006$ over Korean freeway pavement marking at an interval of three months for the period. The mobile measurement system, a laserlux, was employed for the purpose. The DB has provided a lot of information about materials and performance of the specific pavement marking such as geometric features, traffic volumes, material characteristics and the installation date. This study provides the comparison of pavement marking performances under diversified conditions. Based on accumulated pavement marking performances, this study provides performance curves based on the diversified factors. The goal of the retroreflectivity modeling is to develop equations that can be used to estimate an average retroreflectivity of pavement markings as a function time since application and traffic volume. After representing the variation of retroreflectivities and estimating regression curves by linear, exponential, logarithmic and power function, the regression curve which had the highest coefficient of determination and the value similar to the last field measurement was regarded as the retroreflectivity decay model. As a result of verification, the decay model showed the signification within the 90% confidence level and especially showed the clear relation with field data according to increase of cumulative vehicle exposure. Accordingly, these models can be used to determine service lives, retroreflectivity degradation rates, and retroreflectivity of new markings.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4A
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• pp.343-352
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• 2010

Recently, many researchers have been carried out to estimate more controlled service life and long-term performance of carbonated concrete structures. Durability analysis and design based on probability have been induced to new concrete structures for design. This paper provides a carbonation prediction model based on the Fick's 1st law of diffusion using statistic data of carbonated concrete structures and the probabilistic analysis of the durability performance has been carried out by using a Bayes' theorem. The influence of concerned design parameters such as $CO_2$ diffusion coefficient, atmospheric $CO_2$ concentration, absorption quantity of $CO_2$ and the degree of hydration was investigated. Using a monitoring data, this model which was based on probabilistic approach was predicted a carbonation depth and a remaining service life at a variety of environmental concrete structures. Form the result, the application method using a realistic carbonation prediction model can be to estimate erosion-open-time, controlled durability and to determine a making decision for suitable repair and maintenance of carbonated concrete structures.