• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 춘계학술대회 논문집
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• pp.765-771
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• 2008

코레일은 고속철도 운행 이후 RCM을 적용한 신뢰성 정착에 많은 노력을 해 오고 있다. 또한 고속철도에 이어 일반차량 분야의 차종에 대한 검수주기 및 방법 등에 대한 최적화 연구를 수행하고 있다. 일반차종 중 가장 최근의 모델이고 향후 운행량이 늘어날 것으로 예상되는 신형전기 기관차의 검수주기 및 방법에 대한 연구를 진행 중에 있다. 신형전기기관차의 현행 검수주기 및 방법과 동종차량의 검수 현황, 국외 선진국의 동종차량의 검수현황 등을 분석하여 최적의 검수방안을 찾고자 한다. 현행 시행되고 있는 신형전기기관차의 검수주기는 안전성에 우선을 두어 경제성 및 신뢰성이 다소 소홀히 되고 있음을 알 수 있다. 연구를 통해 안전성을 확보함과 동시에 경제성과 신뢰성을 얻을 수 있는 정비방법을 찾고자 한다. 이번 논문에서는 현행 검수 실태의 분석을 통해 문제점과 개선점이 무엇인지를 찾아내는 것으로 하였다. 향후 연구가 종결되는 시점(2008년 말)에서 최적의 대안을 제시할 수 있을 것이다.

• 대한안전경영과학회지
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• 제6권1호
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• pp.61-70
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• 2004

In this paper, a group replacement policy based on a failure count is analysed. For a group of identical repairable units, a maintenance policy is performed with two phase considerations: a repair interval phase and a waiting interval phase. Each unit undergoes minimal repair at failure during the repair interval. Beyond the interval, no repair is made until a number of failures. The expected cost rate expressions under the policy is derived. A method to obtain the optimal values of decision variables are explored. Numerical examples are given to demonstrate the results.

• 산업경영시스템학회지
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• 제26권2호
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• pp.1-8
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• 2003

The operation and management of a plant require proper accounting for the constraints coming from reliability requirements as well as from budget and resource considerations. Most of the mathematical methods to decide the inspection time interval for plant maintenance by reliability theory are too complicated to be solved. Moreover, the mathematical and theoretical models are not usually cases in the practical applications. In order to overcome these problems, we propose a new the decision-making method of optimal inspection interval to minimize the maintenance cost by reliability theory and genetic algorithm (GA). The most merit of the proposed method is to decide the inspection interval for a plant machine of which failure rate $\lambda$(t) conforms to any probability distribution. Therefore, this method is more practical. The efficiency of the proposed method is verified by comparing the results obtained by GA-based method with the inspection model haying regular time interval.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.191-204
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• 2006

This paper develops a Bayesian method to derive the optimal sequential preventive maintenance(PM) policy by determining the PM schedules which minimize the mean cost rate. Such PM schedules are derived based on a general sequential imperfect PM model proposed by Lin, Zuo and Yam(2000) and may have unequal length of PM intervals. To apply the Bayesian approach in this problem, we assume that the failure times follow a Weibull distribution and consider some appropriate prior distributions for the scale and shape parameters of the Weibull model. The solution is proved to be finite and unique under some mild conditions. Numerical examples for the proposed optimal sequential PM policy are presented for illustrative purposes.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 춘계학술대회 논문집
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• pp.1350-1358
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• 2008

Recently the study related to life cycle cost analysis of railway structure consisted of a complex is proceeded covering several range, which is considering the methodology of efficiency and rationalization for maintenance and analysing long-time behavior of the structure of looking at standpoint from asset management and safety. But LCCA(life cycle cost analysis) of railway structure was almost impossible as there were not anything datum for maintenance plan, such as maintenance periods related to each of components(painting and corrosion of steel, and cracking of elements, etc)and maintenance proportion, despite of its 100-year history. According, for collecting data related to railway truss bridge, bridge record cards and testing safety papers, and researching question, etc are surveyed and classified for LCC Analysis. Especially, LCC assessment on the side of assets-maintenance considering about initial cost, maintenance cost, and indirect cost is constructed. Maintenance period and complementary measure rate are very important in maintenance. To decide maintenance period, Baysian updating method is applied.

• 전기학회논문지
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• 제56권8호
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• pp.1382-1389
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• 2007

This paper presents a new approach for solving the problem of maintenance scheduling of generating units using a binary particle swarm optimization (BPSO). In this paper, we find the optimal solution of the maintenance scheduling of generating units within a specific time horizon using a binary particle swarm optimization algorithm, which is the discrete version of a conventional particle swarm optimization. It is shown that the BPSO method proposed in this paper is effective in obtaining feasible solutions in the maintenance scheduling of generating unit. IEEE reliability test systems(1996) including 32-generators are selected as a sample system for the application of the proposed algorithm. From the result, we can conclude that the BPSO can find the optimal solution of the maintenance scheduling of the generating unit with the desirable degree of accuracy and computation time, compared to other heuristic search algorithm such as genetic algorithms. It is also envisaged that BPSO can be easily implemented for similar optimizations and scheduling problems in power system problems to obtain better solutions and improve convergence performance.

• 교육시설
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• 제15권3호
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• pp.4-16
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• 2008

BTL(Build-Transfer-Lease) Project for Education Facilities is contracted as a package which consists of several education facilities and its maintenance period is 20 years. Thus, total cost variation largely depends on the accuracy of the maintenance cost forecasting in the early stage in the life cycle of the BTL Projects. This research develops a method using complete linkage algorithm and branch & bound algorithm to help in finding optimal bundling combination. The result of this research suggests more reasonable and effective forecasting method for the maintenance bundle in BTL projects.

• Structural Monitoring and Maintenance
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• 제2권2호
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• pp.133-143
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• 2015

Civil engineering infrastructure is aging and requires cost-effective maintenance strategies to enable infrastructure systems operate reliably and sustainably. This paper presents an approach for determining risk-cost balanced repair strategy of corrosion damaged reinforced concrete structures with consideration of uncertainty in structural resistance deterioration. On the basis of analytical models of cover concrete cracking evolution and bond strength degradation due to reinforcement corrosion, the effect of reinforcement corrosion on residual load carrying capacity of corroded reinforced concrete structures is investigated. A stochastic deterioration model based on gamma process is adopted to evaluate the probability of failure of structural bearing capacity over the lifetime. Optimal repair planning and maintenance strategies during the service life are determined by balancing the cost for maintenance and the risk of structural failure. The method proposed in this study is then demonstrated by numerical investigations for a concrete structure subjected to reinforcement corrosion. The obtained results show that the proposed method can provide a risk cost optimised repair schedule during the service life of corroded concrete structures.

• Journal of Electrical Engineering and Technology
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• 제5권1호
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• pp.8-15
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• 2010

A new technique using a search method which is based on fuzzy multi-criteria function is proposed for GMS(generator maintenance scheduling) in order to consider multi-objective function. Not only minimization of probabilistic production cost but also maximization of system reliability level are considered for fuzzy multi-criteria function. To obtain an optimal solution for generator maintenance scheduling under fuzzy environment, fuzzy multi-criteria relaxation method(fuzzy search method) is used. The practicality and effectiveness of the proposed approach are demonstrated by simulation studies for a real size power system model in Korea in 2010.

• 한국신뢰성학회지:신뢰성응용연구
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• 제17권3호
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• pp.188-196
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• 2017

Purpose: The purpose of this research is to determine optimal replacement age using non-informative prior information and Bayesian method. Methods: We propose a novel approach using Bayesian method to determine the optimal replacement age in block replacement policy by defining the prior probability with data on failure time and repair time. The Marcov Chain Monte Carlo simulation is used to investigate the asymptotic distribution of posterior parameters. Results: An optimal replacement age of block replacement policy is determined which minimizes cost and nonoperating time when no information on prior distribution of parameters is given. Conclusion: We find the posterior distribution of parameters when lack of information on prior distribution, so that the optimal replacement age which minimizes the total cost and maximizes the total values is determined.