• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.90-96
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• 2000

The technology of preventive maintenance and corrective maintenance is widely applied to ships in order to maintain the good voyageable condition. One of the most important fields of marine engineering is to seek the maximum availability and to solve the stochastic maintenance problem such that the cost for corrective maintenance is minimized. Accordingly, for the purpose of making the most suitable maintenance schedule which minimizes the expected cost function, this paper suggests the method to grasp the failure characteristics by the ship's maintenance data that are collected from the past. And, suggests the method to estimate the optimal maintenance interval by using the dynamic programming and the cumulative hazard rate function attained from the maintenance data.

• Journal of the military operations research society of Korea
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• v.24 no.1
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• pp.98-109
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• 1998

This study develops a maintenance strategy for a reparable 2-unit standby system. The maintenance strategy implies the waiting time to call the repair facility when the unit-1 fails. Almeida and Souza set up the multi-attribute utility function consisting of system availability and repair cost for several maintenance strategies and decide the optimal maintenance strategy that maximize the expected value of the utility function. We decide the optimal maintenance strategy satisfying the following two criteria about the utility function : maximum variance using Almeida and Souza's utility function. It both criteria are not satisfied at the same time for every strategies, the strategy maximizing the lower confidence limit for expected utility function is regarded as an optimal one.

• Smart Structures and Systems
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• v.30 no.6
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• pp.571-582
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• 2022

Optimal life-cycle management is a challenging issue for deteriorating regional bridges. Due to the complexity of regional bridge structural conditions and a large number of inspection and maintenance actions, decision-makers generally choose traditional passive management strategies. They are less efficiency and cost-effectiveness. This paper suggests a deep reinforcement learning framework employing double-deep Q-networks (DDQNs) to improve the life-cycle management of deteriorating regional bridges to tackle these problems. It could produce optimal maintenance plans considering restrictions to maximize maintenance cost-effectiveness to the greatest extent possible. DDQNs method could handle the problem of the overestimation of Q-values in the Nature DQNs. This study also identifies regional bridge deterioration characteristics and the consequence of scheduled maintenance from years of inspection data. To validate the proposed method, a case study containing hundreds of bridges is used to develop optimal life-cycle management strategies. The optimization solutions recommend fewer replacement actions and prefer preventative repair actions when bridges are damaged or are expected to be damaged. By employing the optimal life-cycle regional maintenance strategies, the conditions of bridges can be controlled to a good level. Compared to the nature DQNs, DDQNs offer an optimized scheme containing fewer low-condition bridges and a more costeffective life-cycle management plan.

• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.291-301
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• 2009

This paper presents the optimal design and analysis method of the train overhaul maintenance facility based on the simulation. Because the train is composed of a coach or more, we design the simulation model after analyzing the operation of train into train, coach, coach's body parts and wheel parts and soon. In simulation analysis, we consider the critical (dependent) factors and design (independent) parameters for the selection of alternatives and optimal design. Therefore, Multi Criteria Decision Making (MCDM) is proposed for the selection of alternatives and optimal method in order to find the optimal design factors. The case study for the above approach is used for the electronic locomotive overhaul maintenance facility. This paper provides a comprehensive framework for the train overhaul maintenance facility design using the simulation, MCDM and optimal methods. Therefore, the method developed for this research can be adopted for other enhancements in different but comparable situation.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.338-346
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• 2016

In order for the optimal solution of generators’ annual maintenance scheduling to be applicable to the actual power system it is crucial to incorporate the constraints related to the equivalent operation hours (EOHs) in the optimization model. However, most of the existing researches on the optimal maintenance scheduling are based on the assumption that the maintenances are to be performed periodically regardless of the operation hours. It is mainly because the computation time to calculate EOHs increases exponentially as the number of generators becomes larger. In this paper an efficient algorithm based on demand grouping method is proposed to calculate the approximate EOHs in an acceptable computation time. The method to calculate the approximate EOHs is incorporated into the optimization model for the maintenance scheduling with consideration on the EOHs of generators. The proposed method is successfully applied to the actual Korean power system and shows significant improvement when compared to the result of the maintenance scheduling algorithm without consideration on EOHs.

• 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.

• Journal of Korean Society for Quality Management
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• v.31 no.1
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• pp.142-147
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• 2003

Burn-in is a widely used method to eliminate the initial failures. Preventive maintenance policy such as block replacement with minimal repair at failure is often used in field operation. In this, paper burn-in and maintenance policy are taken into consideration at the same time. The cost of a minimal repair is assumed to be a non-decreasing function of its age. The problems of determining optimal burn-in times and optimal maintenance policy are considered.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.3-157
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• 2001

In this paper, the optimal maintenance scheduling for turbine with considering maintenance replacement rate was proposed in order to reduce the maintenance cost during the whole period of operation, meanwhile keeping current reliability of turbine. The proposed method is only based on a few limited available data with various factors relating to maintenance replacement and repair of turbine. The proposed method will be adopted by Kyushu Electric Power Co., Inc. from April in 2002 to determine the maintenance schedule of thermal power plants.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1511-1519
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• 2013

This paper proposes the allocation method for capacitor-reactor banks in a distribution system with dispersed generators to reduce the installation costs, the maintenance costs and minimize the loss of electrical energy. The expected lifetime and maintenance period of devices with moving parts depends on the total number of operations, which affects the replacement and maintenance period for aging equipment under a limited budget. In this paper, the expected device lifetimes and the maintenance period are included in the formulation, and the optimal operation status of the devices is determined using a genetic algorithm. The optimal numbers and locations for capacitor-reactor banks are determined based on the optimal operation status. Simulation results in a 69-bus distribution system with the dispersed generator show that the proposed technique performs better than conventional methods.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.814-823
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• 2012

Electric power transmission utilities make an effort to maximize profit by reducing their electricity supply and operation costs while maintaining their reliability. The development of maintenance strategies for aged components is one of the more effective ways to achieve this goal. The reliability centered approach is a key method in providing optimal maintenance strategies. It considers the tradeoffs between the upfront maintenance costs and the potential costs incurred by reliability losses. This paper discusses the application of the Particle Swarm Optimization (PSO) technique used to find the optimal maintenance strategy for a transmission component in order to achieve the minimum total expected cost composed of Generation Cost (GC), Maintenance Cost (MC), Repair Cost (RC) and Outage Cost (OC). Three components of a transmission system are considered: overhead lines, underground cables and insulators are considered. In regards to aged and aging component, a component state model that uses a modified Markov chain is proposed. A simulation has been performed on an IEEE 9-bus system. The results from this simulation are quite encouraging, and then the proposed approach will be useful in practical maintenance scheduling.