• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.219-235
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• 2019

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.10
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• pp.895-900
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• 2016

Remaining useful life (RUL) estimation of the Li-ion battery has gained great interest because it is necessary for quality assurance, operation planning, and determination of the exchange period. This paper presents the RUL estimation of an Li-ion battery for an energy storage system using exponential function for the degradation model and Markov Chain Monte Carlo (MCMC) approach for parameter estimation. The MCMC approach is dependent upon information such as model initial parameters and input setting parameters which highly affect the estimation result. To overcome this difficulty, this paper offers a guideline for model initial parameters based on the regression result, and MCMC input parameters derived by comparisons with a thorough search of theoretical results.

• Nuclear Engineering and Technology
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• v.56 no.11
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• pp.4905-4913
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• 2024

This study focuses on the Structure, System, and Components (SSC) of G.A Siwabessy Multipurpose Reactor (RSG-GAS), emphasizing the integration of reliability improvement within asset management optimization. The research aims to derive Maintenance Priority Index (MPI) values, contributing to system reliability ratings, crucial for assessing component functionality and estimating Remaining Useful Life (RUL) The methodology involves measuring critical aspects of system quality, safety, and cost. The MPI value, representing 10 % of SSC critical components, guides subsequent reliability calculations. Utilizing failure data from RSG-GAS components (2010-2018) and reactor core configurations (70th-96th core), SSC reliability is simulated using Monte Carlo simulation, with RUL based on real data. High MPI values are identified for critical components such as KBE01/AP01-02_B in the primary purification system, JE-01/AP01-02_A in the primary cooling system, and PA01-02/CR001_A in the secondary cooling system. Maintenance intervals of 100 days are recommended for KBE01/AP 01-02_B and JE-01/AP01-02_A, exceeding 50 % reliability, while PA01-02/CR001_A maintenance can extend to 75 days. Monte Carlo simulation results, with 1000 samples, closely align with real reliability values. The RUL result projects operational lifetimes of 225.6, 221.4, and 171.5 days for KBE01/AP 01-02_B, JE-01/AP01-02_A, and PA01-02/CR001_A components. This study improves asset management, offering practical insights for critical safety system component maintenance planning.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.271-280
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• 2019

Smart factory, a critical part of digital transformation, enables data-driven decision making using monitoring, analysis and prediction. Predictive maintenance is a key element of smart factory and the need is increasing. The purpose of this study is to analyze the degradation characteristics of a galvanizing kettle for the steel plating process and to predict the remaining useful life(RUL) for predictive maintenance. Correlation analysis, multiple regression, principal component regression were used for analyzing factors of the process. To identify the trend of degradation, a proposed rolling window was used. It was observed the degradation trend was dependent on environmental temperature as well as production factors. It is expected that the proposed method in this study will be an example to identify the trend of degradation of the facility and enable more consistent predictive maintenance.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.8
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• pp.1021-1027
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• 2013

In this study, a prognostic technique for cavitation erosion that is applicable to centrifugal pumps is devised. To estimate the erosion states of pumps, damage rates are calculated based on cavitation noise measurements. The accumulated damage is predicted by using Miner's rule and the estimated damage undergone when coping with particular operating conditions. The remaining useful life (RUL) of the pump impellers is estimated according to the accumulated damage prediction and based on the assumption of future operating conditions. A Monte Carlo simulation is applied to obtain a prognostic uncertainty. The comparison of the prediction and the test results demonstrates that the developed method can be applied to predict cavitation erosion states and RUL estimates.

• Journal of IKEEE
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• v.26 no.4
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• pp.531-536
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• 2022

In order to efficeintly manage a battery, it is important to accurately estimate and manage the SOH(State of Health) and RUL(Remaining Useful Life) of the batteries. Even if the batteries are of the same type, the characteristics such as facility capacity and voltage are different, and when the battery for the training model and the battery for prediction through the model are different, there is a limit to measuring the accuracy. In this paper, We proposed the entropy index using voltage distribution and discharge time is generalized, and four batteries are defined as a training set and a test set alternately one by one to predict the health status of batteries through linear regression analysis of machine learning. The proposed method showed a high accuracy of more than 95% using the MAPE(Mean Absolute Percentage Error).

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1472-1479
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• 2024

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.4
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• pp.355-362
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• 2012

Remaining useful life(RUL) prediction of a system is important in the prognostics field since it is directly linked with safety and maintenance scheduling. In the physics-based prognostics, accurately estimated model parameters can predict the remaining useful life exactly. It, however, is not a simple task to estimate the model parameters because most real system have multivariate model parameters, also they are correlated each other. This paper presents representative methods to estimate model parameters in the physics-based prognostics and discusses the difference between three methods; the particle filter method(PF), the overall Bayesian method(OBM), and the sequential Bayesian method(SBM). The three methods are based on the same theoretical background, the Bayesian estimation technique, but the methods are distinguished from each other in the sampling methods or uncertainty analysis process. Therefore, a simple physical model as an easy task and the Paris model for crack growth problem are used to discuss the difference between the three methods, and the performance of each method evaluated by using established prognostics metrics is compared.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.192-198
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• 2021

The battery's State of Health (SOH) is a critical parameter in the process of battery use, as it represents the Remaining Useful Life (RUL) of the battery. Electrochemical Impedance Spectroscopy (EIS) is a widely used technique in observing the state of the battery. The measured impedance at certain frequencies can be used to evaluate the state of the battery, as it is intimately tied to the underlying chemical reactions. In this work, a low-cost portable EIS instrument is developed on the basis of the ARM Cortex-M4 Microcontroller Unit (MCU) for measuring the impedance spectrum of Li-ion battery packs. The MCU uses a built-in DAC module to generate the sinusoidal sweep perturbation signal. Moreover, it performs the dual-channel acquisition of voltage and current signals, calculates impedance using a Digital Lock-in Amplifier (DLA), and transmits the result to a PC. By using LabVIEW, an interface was developed with the real-time display of the EIS information. The developed instrument was suitable for measuring the impedance spectrum of the battery pack up to 1000 V. The measurement frequency range of the instrument was from 1 hz to 1 Khz. Then, to prove the performance of the developed system, the impedance of a Samsung SM3 battery pack and a Bexel pouch module were measured and compared with those obtained by the commercial instrument.