• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.60-66
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• 2021

The importance of maintenance is leading the application of the maintenance strategy in various industries. The maritime industry is also a part of them, with changes in selecting and applying the maintenance strategy, but rather slowly, by retaining the old strategy. In particular, the ship is maintaining a previously used strategy. In the circumstance of the sea, ship requires a new suggestion for maintenance strategy. A ship predictive maintenance model predicts the breakdown or malfunction of machineries to secure maintenance time with preventive actions and treatments, thereby avoiding maintenance-related dangerous factors. This study focused on applying text analysis to an Liquefied Natural Gas Carriers dock indent document, and the analysis results were interpreted from the original document. The inter-relational patterns observed from the frequency of common maintenance combinations among different parts and equipment in ships will be applied to the development of ship predictive maintenance.

• Journal of the Korean Society for Railway
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• v.15 no.5
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• pp.459-466
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• 2012

Due to the issues such as omission of data, document based management, maintenance based on measurement data and wire-based network, it is difficult existing maintenance system for railway bridges to act to diverse characteristics of site and environmental changes in real time. With these reasons, there are many constraints in establishing active maintenance strategies for railway bridges. To solve these issues, this study suggests an integrated maintenance business model based on practical utilization and information management based on BIM technology to build a smart maintenance operation system based on ubiquitous computing for railway bridges. To secure its development and practical applications, a quantitative evaluation by questionnaire analysis was performed. Therefore, it is expected that the suggested model will be utilized as a framework model in order to build the smart maintenance operation system from collection of maintenance data to action for railway bridges.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.157-162
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• 2004

Recently, Life Cycle Cost (LCC) for civil infrastructures, such as pavements, bridges, and dams, has been emphasized. However there are few cost models for road tunnel especially for maintenance phase. The road network is composed of highways, bridges, and road tunnels. Thus it is as important as for road tunnels to keep safe for traffic. The maintenance strategies for road tunnels can be achieved based on the minimization of LCC in maintenance phase. For this purpose, in this paper, cost model and cost classification for road tunnel in maintenance phase are suggested.

• International Journal of Reliability and Applications
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• v.12 no.1
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• pp.41-48
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• 2011

This paper considers the maintenance model suggested by Jung and Park (2010) to adopt the Bayesian approach and obtain an optimal replacement policy following the expiration of NFRRW. As the criteria to determine the optimal maintenance period, we use the expected cost during the life cycle of the system. When the failure times are assumed to follow a Weibull distribution with unknown parameters, we propose an optimal maintenance policy based on the Bayesian approach. Also, we describe the revision of uncertainty about parameters in the light of data observed. Some numerical examples are presented for illustrative purpose.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.173-179
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• 1992

This paper is concerned with preventive maintenance model for the items whose failures are dependent on their wear level. The previous maintenance models have used time as their decision variable, but it is not appropriate for the case which have wear dependent processes for their failures. In this paper, we consider an operating item which is under periodic review and which is subject to degradation. The scheduled maintenance (overhaul ) is based on the level of item degradation rather time. A functional equation for the total expected cost over an infinite horizon period is formulated and solved.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.2
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• pp.67-73
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• 2012

This paper proposes optimal maintenance strategies for power distribution systems that involve the use of the reliability-centered maintenance (RCM) method. We developed an improved decision model based on the Markov process. This model can obtain the optimal inspection interval and maintenance method based on the total expected cost. We used ordinal optimization for solving the optimal problem. Optimal maintenance strategies were presented by applying the developed method to the RBTS model. A B/C analysis proved that these strategies offer maximum benefit-to-cost.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.99-106
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• 1999

This paper considers an imperfect repair model for which the repairable system is maintained preventively at periodic times and is replaced by a new system when a predetermined number of preventive maintenance has been applied. our main objective of this is to determine the optimal number of preventive maintenances before the system is replaced and the optimal length of interval between two consecutive preventive maintenances under a new repair model which is referred to as an ineffective preventive maintenance. Such a model assumes a periodic preventive maintenance in which the system is effectively maintained with a certain probability. Otherwise the system is not improved at all after each maintenance and thus the failure rate remains the same as before. The criteria to determine the optimal number of preventive maintenances and length of period is the expected cost rate per unit time for an infinite time span. We give the explicit expressions for the expected cost rate per unit time. Some numerical examples are presented for illustrative purposes.

• Journal of the Korean Society of Safety
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• v.12 no.4
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• pp.21-26
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• 1997

Korea Atomic Energy Research Institute (KAERI) developed a risk monitor called Risk Monster which supports for plant operators and maintenance schedulers to monitor plant risk and to avoid high peak risk by rearranging maintenance work schedule. Risk Monster can update the plant risk continuously according to the change of system/component configuration since Risk Monster reevaluates the plant risk based on the Probabilistic Safety Assessment (PSA) results. A brief description of Risk Monster is provided. The PSA model of UCN 3, 4 nuclear power plant was converted by KAERI to Risk Monster model. Using this Risk Monster model, a feasibility study of the on-line maintenance of an Essential Service Water (ESW) pump was performed. On-line maintenance of one ESW pump has been shown to be acceptably safe, and has economic benefits. In addition, it is not a violation of technical specification to continue plant operation with an out-of-service ESW pump.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.33-49
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• 2002

CBM (Condition-Based Maintenance) has increasingly drawn attention in industry because of its many benefits. CBM Problem Is characterized as a state-dependent scheduling model that demands simultaneous maintenance actions, each for an attribute that influences on machine condition. This problem is very hard to solve within conventional Markov decision process framework. In this paper, we present an intelligent machine maintenance scheduler, for which a new incremental decision tree learning method as evolutionary system identification model and shortest path problem as schedule generation model are developed. Although our approach does not guarantee an optimal scheduling policy in mathematical viewpoint, we verified through simulation based experiment that the intelligent scheduler is capable of providing good scheduling policy that can be used in practice.

• IE interfaces
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• v.21 no.3
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• pp.254-261
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• 2008

Connected-(r,s)-out of-(m,n) : F system is an important topic in redundancy design of the complex system reliability and it's maintenance policy. Previous studies applied Monte Carlo simulation and genetic, simulated annealing algorithms to tackle the difficulty of maintenance policy problem. These algorithms suggested most suitable maintenance cycle to optimize maintenance pattern of connected-(r,s)-out of-(m,n) : F system. However, genetic algorithm is required long execution time relatively and simulated annealing has improved computational time but rather poor solutions. In this paper, we propose the ant colony optimization approach for connected-(r,s)-out of-(m,n) : F system that determines maintenance cycle and minimum unit cost. Computational results prove that ant colony optimization algorithm is superior to genetic algorithm, simulated annealing and tabu search in both execution time and quality of solution.