• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.4
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• pp.251-260
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• 2020

A growing interest has emerged in recycling used automobile batteries into energy storage systems (ESSs) to prevent their harmful effects to the environment from improper disposal and to recycle such resources. To transform used batteries into ESSs, composing battery modules with similar performance by grading them is crucial. Imbalance among battery modules degrades the performance of an entire system. Thus, the selection of modules with similar performance and remaining life is the first prerequisite in the reuse of used batteries. In this study, we develop an instrument to measure the impedance spectrum of a battery module to predict the useful remaining life of the used battery. The developed hardware and software are used to apply the AC perturbation to the used battery module and measure its impedance spectrum. The developed instrument can measure the impedance spectrum of the battery module from 0.1 Hz to 1 kHz and calculate the equivalent circuit parameters through curve fitting. The performance of the developed instrument is verified by comparing the measured impedance spectra with those obtained by a commercial equipment.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.04a
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• pp.682-687
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• 2000

In this research the remaining service life of the concrete due to the steel corrosion was predicted by three cases; causing carbonation, using sea sand, using deicing salts. In case of deterioration by carbonation, effective carbonation depth, effective coverage depth and relative humidity are considered for predicting method. In case of using sea sand, predicting method is made of rust growth equation from polarization resistance method. In case of using deicing salts, predicting method is made of transformation of Fick's law. Three methods are very useful in predicting service life of concrete.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.142-148
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• 2015

Due to advancements in technology and manufacturing capability, it is not uncommon that life tests yield no or few failures at low stress levels. In these situations it is difficult to analyse lifetime data and make meaningful inferences about product or system reliability. For some products or systems whose performance characteristics degrade over time, a failure is said to have occurred when a performance characteristic crosses a critical threshold. The measurements of the degradation characteristic contain much useful and credible information about product or system reliability. Degradation measurements of the performance characteristics of an unfailed unit at different times can directly relate reliability measures to physical characteristics. Reliability prediction based on physical performance measures can be an efficient and alternative method to estimate for some highly reliable parts or systems. If the degradation process and the distance between the last measurement and a specified threshold can be established, the remaining useful life is predicted in advance. In turn, this prediction leads to just in time maintenance decision to protect systems. In this paper, we describe techniques for mapping product or system which has degrading performance parameter to the associated classical reliability measures in the performance domain. This paper described a general modeling and analysis procedure for reliability prediction based on one dominant degradation performance characteristic considering pseudo degradation performance life trend model. This pseudo degradation trend model is based on probability modeling of a failure mechanism degradation trend and comparison of a projected distribution to pre-defined critical soft failure point in time or cycle.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.4
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• pp.313-322
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• 2013

Batteries, which are being used as energy sources in various applications, tend to degrade, and their capacity declines with repeated charging and discharging cycles. A battery is considered to fail when it reaches 80% of its initial capacity. To predict this, prognosis techniques are attracting attention in recent years in the battery community. In this study, a method is proposed for estimating the battery health and predicting its remaining useful life (RUL) based on the slope of the charge voltage curve. During this process, a Bayesian framework is employed to manage various uncertainties, and a Particle Filter (PF) algorithm is applied to estimate the degradation of the model parameters and to predict the RUL in the form of a probability distribution. Two sets of test data-one from the NASA Ames Research Center and another from our own experiment-for an Li-ion battery are used for illustrating this technique. As a result of the study, it is concluded that the slope can be a good indicator of the battery health and PF is a useful tool for the reliable prediction of RUL.

• 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.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.5
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• pp.489-496
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• 2010

The power generation and chemical industry have been subjected to further material degradation with long term operations and need to predict the remaining service life of components, such as reformer tube and steam turbine rotor, that have operated at elevated temperatures. As a non-destructive technique, replication method with reliable metallurgical life and microstructural soundness assessment has been recognized with strongly useful method until now. Developments of this method have variously accomplished by new quantitative approach, such as carbide analysis, with A-parameter and grain deformation method. An overview of replication, some new techniques for material degradation and life assessment were introduced in this paper. Also, on-site applications and its reasonableness were described. As a result of having analyzed microstructure by replication method, carbide approach was quantitatively useful to life assessment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.15-16
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• 2020

본 논문에서는 산업 현장에서 사용되는 기계들의 건전성을 유지하고 예측하는 시스템을 개선할 수 있는 연구 결과를 비교하고 설명한다. 이번 연구에서는 딥러닝 기술을 이용함으로서 특정장치에 종속되지 않고 범용적으로 수집된 소음데이터를 사용하여 현장 적용의 유연성을 높이고, 딥러닝 모델 중 GRU를 이용하여 기존 연구 결과와 비교 실험을 하여 더 우수한 결과를 얻었다.

• 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.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.455-464
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• 2020

Reinforcement corrosion is one of the major problems in the durability of reinforced concrete structures exposed to aggressive environments. Deterioration caused by reinforcement corrosion reduces the durability and the safety margin of concrete structures, causing excessive costs in managing these structures safely. This paper aims to investigate the effects of reinforcement corrosion on the load bearing capacity deterioration of the corroded reinforced concrete structures. A new analytical method is proposed to predict the crack growth of cover concrete and evaluate the residual strength of concrete structures with corroded reinforcement failing in bond. The structural performance indicators, such as concrete crack growth and flexural strength deterioration rate, are assumed to be a stochastic process for lifetime distribution modelling of structural performance deterioration over time during the life cycle. The Weibull life evolution model is employed for analysing lifetime reliability and estimating remaining useful life of the corroded concrete structures. The results for the worked example show that the proposed approach can provide a reliable method for lifetime performance assessment of the corroded reinforced concrete structures.