• Computers and Concrete
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• v.18 no.1
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• pp.69-97
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• 2016

Performance-based remaining life assessment of reinforced concrete bridge girders, subject to chloride-induced corrosion of reinforcement, is addressed in this paper. Towards this, a methodology that takes into consideration the human judgmental aspects in expert decision making regarding condition state assessment is proposed. The condition of the bridge girder is specified by the assignment of a condition state from a set of predefined condition states, considering both serviceability- and ultimate- limit states, and, the performance of the bridge girder is described using performability measure. A non-homogeneous Markov chain is used for modelling the stochastic evolution of condition state of the bridge girder with time. The thinking process of the expert in condition state assessment is modelled within a probabilistic framework using Brunswikian theory and probabilistic mental models. The remaining life is determined as the time over which the performance of the girder is above the required performance level. The usefulness of the methodology is illustrated through the remaining life assessment of a reinforced concrete T-beam bridge girder.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2401-2406
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• 2015

Pipelines are used by large heavy industries to deliver various types of fluids. Since this is important to maintain the performance of large systems, it is necessary to accurately predict remaining life of the corroded pipeline. However, predicting the remaining life is difficult due to uncertainties in the associated variables, such as geometries, material properties, corrosion rate, etc. In this paper, a statistical method for predicting corrosion remaining life is proposed using Bayesian inference. To accomplish this, pipeline failure probability was calculated using prior information about pipeline failure pressure according to elapsed time, and the given experimental data based on Bayes' rule. The corrosion remaining life was calculated as the elapsed time with 10 % failure probability. Using 10 and 50 samples generated from random variables affecting the corrosion of the pipe, the pipeline failure probability was estimated, after which the estimated remaining useful life was compared with the assumed true remaining useful life.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.1-7
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• 2018

The Continuous Ship Unloader (CSU) is an equipment that unloads freight from the ship docked in the port to the land. And the design target life time is designed to be 30 to 50 years, and it is classified as a semi-permanent large facility. However, cracks may occur due to structural defects, abnormal loads, and corrosion, and fatigue failure may occur before the design life is reached. In this study, we predicted the remaining life time of the main component of the CSU considering crack. And also proposed inspection cycle for maintenance of CSU based on the results of the remaining life time prediction. For this purpose, the structure, operational stresses of the CSU were analyzed and main members were selected. And tensile tests and fatigue crack propagation tests were performed with SM490YA and SM570TMC, which are used as main materials for CSU.

• Journal of Korea Technology Innovation Society
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• v.15 no.4
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• pp.745-765
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• 2012

When attempting to use the income approach for the purpose of technology valuation, it is essential to identify the economic life of the technology in question. From the mid-2000s up to the present, the methods proposed by major Korean institutions for estimating the economic life of technologies have been based on cited patent life (CLT), which is one of the types of technology life. The present study utilizes cited patent life (CLT) to estimate the economic life of technology for the purpose of technology valuation, and proposes a new method of analyzing cited patent life, a method that has been improved by taking into consideration the elapsed period and the time period of investment required for commercialization, two factors which have been hitherto overlooked. Survival curve analysis is a method that has already been widely utilized to estimate the economic life of tangible assets, and this study applies the same method to the calculation of the cited patent life index of technology to provide a more objective method for determining the lifetime of a technology. The remaining life expectancy of cited patent life based on the number of elapsed years was calculated and used to determine the life expectancy of a technology that has reached a specific number of elapsed years, which is referred to as the remaining cited-patent life time (r-CLT).

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1999.05a
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• pp.171-176
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• 1999

Nondestructive inspection and accompanying life analysis based on fracture mechanics were the major conventional methods for evaluating remaining life of critical high temperature components in power plants. By using these conventional methods, it has been difficult to perform in-service inspection for life prediction. Also, quantitative damage evaluation due to unexpected abrupt changes in operating temperature was almost impossible. Thus, many efforts have been made for evaluating remaining life during operation of the plants and predicting real-time life usage values based on the shape of structures, operating history, and material properties. In this study, a core software for on-line life monitoring system which carries out real-time life evaluation of a critical component in power boiler(high temperature steam headers) is developed. The software is capable of evaluating creep and fatigue life usage from the real-time stress data calculated by using temperature/stress transfer Green functions derived for the specific headers and by counting transient cycles. The major benefits of the developed software lie in determining future operating schedule, inspection interval, and replacement plan by monitoring real-time life usage based on prior operating history.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.234-239
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• 2004

A study on the probabilistic methodology for the estimation of the remaining life of Pressurized pipelines containing active corrosion defects is presented. This reliability assessment is earned out using extream value distribution of the corroded defects instead of already published failure perssure moded like NG18 or ASME B31G. The failure probability of pipelines depends on the number of corroded defects. and it could be calculated directly as the area exceeded a defined L V(Limited Value of corrosion depth). The remaining life of pressurized pipelines can also be estimated by the PDF of extream value distribution as calculating the exceeded area with a defined failure probability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.18-30
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• 2022

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.1037-1040
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• 2008

In order to predict the remaining life of marine concrete structures under climatic loads, it is necessary to develop an analytical approach to predict the time and space dependent deterioration of concrete structures due to mainly chloride attack up to corrosion initiation and additional deterioration like cracking of cover concrete. This study aims to introduce FEM model for life-time simulation of concrete structures subjected to chloride attack. In order to consider uncertainties in materials as well as environmental parameters for the prediction, Monte Carlo Simulation is integrated in that FEM modeling for reliability-based remaining service life prediction. The paper is organized as follows: firstly general scheme for reliability-based remaining service life of concrete structures is introduced, then the FEM models for chloride penetration, corrosion product expansion and cover cracking are briefly explained, finally an example is demonstrated and the effects of localization of chloride concentration and corrosion product expansion on service life using above model are discussed.

• The Journal of Information Systems
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• v.29 no.1
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• pp.75-91
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• 2020

Purpose Retrofit, defined to be addition of new technologies or features to the old system to increase efficiency or to abate GHG emissions, is considered as an important alternative for the old coal-fired power plant. The purpose of this study is to propose mathematical method to model multiple alternative retrofit in Generation Capacity Expansion Planning(GCEP) problem, and to get insight to the retrofit patterns from realistic case studies. Design/methodology/approach This study made a multi-alternative retrofit GECP model by adopting some new variables and equations to the existing GECP model. Added variables and equations are to ensure the retrofit feature that the life time of retrofitted plant is the remaining life time of the old power plant. We formulated such that multiple retrofit alternatives are simultaneously compared and the best retrofit alternative can be selected. And we found that old approach to model retrofit has a problem that old plant with long remaining life time is retrofitted earlier than the one with short remaining life time, fixed the problem by some constraints with some binary variables. Therefore, the proposed model is formulated into a mixed binary programming problem, and coded and run using the GAMS/cplex. Findings According to the empirical analysis result, we found that approach to model the multiple alternative retrofit proposed in this study is comparing simultaneously multiple retrofit alternatives and select the best retrofit satisfying the retrofit features related to the life time. And we found that retrofit order problem is cleared. In addition, the model is expected to be very useful in evaluating and developing the national policies concerning coal-fired power plant retrofit.