• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.149-154
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• 2008

Service life of bridges should be evaluated by physical life considering damage/deterioration. But it is difficult to identify optimal maintenance scenario due to insufficient research related to that. To identify optimal maintenance scenario, it is needed to develope life cycle profile model of condition state variation by deterioration factor. The LCP model has been developed in consideration of regression analysis and survey in this study. It is expected that the LCP model could help to achieve HBMS system improvement.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.87-96
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• 2019

Purpose: The purpose of this paper is to determine an optimal production time for economic production quantity model with preventive maintenance and random defective rate as the function of a machinery deteriorates. Methods: If a machinery shifts from "in-control" state to "out-of-control" state, a proportion of defective items being produced increases. It is assumed that time to state shift is a random variable and follows an arbitrary distribution. The elapsed time until process shift decreases stochastically as a production cycle repeats and quasi-renewal process is used to implement for production facilities to deteriorate. Results: When the exponential parameter for exponential distribution increases, the optimal production time increases. When Weibull distribution is considered, the optimal production time is closely affected by the shape parameter of Weibull distribution. Conclusion: A mathematical model is suggested to find optimal production time and optimal number of production cycles and numerical examples are implemented to validate the patterns for changes of optimal times under different parameters assumptions. The real application is implemented using the proposed approach.

• 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 Korean Society of Safety
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• v.39 no.1
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• pp.33-40
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• 2024

This study analyzes the impact of regular preventive maintenance (PM) on reducing the failure rate and occurrence of falling accidents of industrial overhead doors. A reliable safety device model with an additional safety device, which is installed to replace a defective one, is proposed. The research methodology involves collecting breakdown and falling accident records, comparing and analyzing data before and after regular PM implementation, and experimenting with two types of retrofittable safety devices. Key findings are as follows. 1. Regular PM implementation significantly reduces the failure rate of old overhead doors. 2. A parallel structured model with two alternative safety devices can minimize falling accident risks. The study's contributions include the following. 1. The positive impact of PM on extending overhead door lifespan is quantified. 2. A general safety device model that can be retrofitted and used as replacement with a fail-safe function is proposed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.4
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• pp.107-114
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• 2008

Currently, each managing agencies are enforcing the maintenance against the cutting slopes, but the universality and objectivity are insufficient, because the evaluation item and model are various, the access method is not mutual-supplementary. Consequently, this study will lead the approach for a rational model development, by the analysis against the existing cutting slope evaluation technique, excavate a collapse primary reason and a factor, by the collapse example analysis, and make out evaluation table to decide a investigation priority of the existing cutting slope in the Investigation Evaluation step using AHP.

• Structural Monitoring and Maintenance
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• v.3 no.4
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• pp.349-375
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• 2016

The tension of an arch bridge hanger is estimated using a number of experimentally identified modal frequencies. The hanger is connected through metallic plates to the bridge deck and arch. Two different categories of model classes are considered to simulate the vibrations of the hanger: an analytical model based on the Euler-Bernoulli beam theory, and a high-fidelity finite element (FE) model. A Bayesian parameter estimation and model selection method is used to discriminate between models, select the best model, and estimate the hanger tension and its uncertainty. It is demonstrated that the end plate connections and boundary conditions of the hanger due to the flexibility of the deck/arch significantly affect the estimate of the axial load and its uncertainty. A fixed-end high fidelity FE model of the hanger underestimates the hanger tension by more than 20 compared to a baseline FE model with flexible supports. Simplified beam models can give fairly accurate results, close to the ones obtained from the high fidelity FE model with flexible support conditions, provided that the concept of equivalent length is introduced and/or end rotational springs are included to simulate the flexibility of the hanger ends. The effect of the number of experimentally identified modal frequencies on the estimates of the hanger tension and its uncertainty is investigated.

• Korean Journal of Construction Engineering and Management
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• v.15 no.1
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• pp.51-61
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• 2014

Preventive maintenance of buildings has increased the importance of interest in that it is able to maintain the performance building has and to prevent a problem occurred in future. For improved preventive maintenance work, it should be performed to select works order clearly and preceded the accurate measurement for the state of works order. when measuring the conditions, measurement of the state of work order considering the various criteria is more effective than to measure by only criterion. But, there are something hard to evaluate exactly between the criteria because of decision-maker's subjective judgments. To solve these problems, this research proposes decision making support model to determine preventive maintenance works order using Fuzzy-sets. By using Fuzzy-sets when measuring state of work objects, it can be reduced vagueness of judgments by decision-makers. This model can be used as a tool for objective evaluation of preventive maintenance work orders and offer the guideline to perform decision-making.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.237-250
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• 2017

This study focuses on the maintenance of the registration details of the cadastral study (drawings, chiefs) in relation to Uiwang city real estate administration information unification project by the data maintenance business. The purpose of this study is to provide high quality data and improve the efficiency of data maintenance business in the unification of real estate administration information together with the intellectual study diffusion maintenance model in the future. In this study, based on the results of the intellectual study data maintenance project and the effectiveness of institutional, temporal and cost aspects, it was able to show the effect of the data maintenance project. And analyzed the current situation, typed the error shown here, and developed the maintenance plan and maintenance result.

• Journal of the Society of Disaster Information
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• v.16 no.2
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• pp.276-290
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• 2020

Purpose: The construction history of domestic port facilities has been more than 100 years, and until recently, modern facilities have been continuously built and expanded. However, it is not easy to keep the required performance conditions at the time of initial construction due to changes in the marine environment and increase in volume. In particular, in the case of harbor structures that have a long service life, safety performance and function management are becoming very important due to the increase in the size of ships, the increasing frequency of use, and the increase in the scale of natural disasters. Method: Therefore, this study investigates the state change by structural type of port facilities and analyzes the rehabilitation activities and the history that contribute to the performance improvement and life extension activities. Result: Through this, we distinguished between performance improvement cost (CAPEX) and repair maintenance activity (OPEX) that can be used to establish port facility maintenance strategy, and suggested cost model that can be used to establish maintenance strategy. Conclusion: These studies are expected to contribute greatly to mid- to long-term investment decisions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.611-620
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• 2021

The maintenance industry is mainly progressing based on condition-based maintenance after corrective maintenance and preventive maintenance. In condition-based maintenance, maintenance is performed at the optimum time based on the condition of equipment. In order to find the optimal maintenance point, it is important to accurately understand the condition of the equipment, especially the remaining useful life. Thus, using simulation data (C-MAPSS), a prediction model is proposed to predict the remaining useful life of a turbofan engine. For the modeling process, a C-MAPSS dataset was preprocessed, transformed, and predicted. Data pre-processing was performed through piecewise RUL, moving average filters, and standardization. The remaining useful life was predicted using principal component analysis and the k-NN method. In order to derive the optimal performance, the number of principal components and the number of neighbor data for the k-NN method were determined through 5-fold cross validation. The validity of the prediction results was analyzed through a scoring function while considering the usefulness of prior prediction and the incompatibility of post prediction. In addition, the usefulness of the RUL prediction model was proven through comparison with the prediction performance of other neural network-based algorithms.