• 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.

• 대한기계학회논문집A
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• 제31권1호
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• pp.26-35
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• 2007

The vehicle of urban transit is a complex system that consists of various electric, electronic, and mechanical equipments, and the maintenance cost of this complex and large-scale system generally occupies sixty percent of the LCC (Life Cycle Cost). For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. The concept of system reliability has been introduced and optimized as the key of reasonable maintenance strategies. For optimization, three preceding studies were accomplished; standardizing a maintenance classification, constructing RBD (Reliability Block Diagram) of VVVF (Variable Voltage Variable Frequency) urban transit, and developing a web based reliability evaluation system. Historical maintenance data in terms of reliability index can be derived from the web based reliability evaluation system. In this paper, we propose applying inverse problem analysis method and hybrid neuro-genetic algorithm to system reliability optimization for using historical maintenance data in database of web based system. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between several component reliability (input) and system reliability (output) of structural system. The inverse problem can be formulated by using neural network. One of the neural network training algorithms, the back propagation algorithm, can attain stable and quick convergence during training process. Genetic algorithm is used to find the minimum square error.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.618-629
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• 2006

This Paper is about optimizing preventive maintenance period of connected (r,s) out of(m,n) : F lattice system that one of multi-component system, (m,n) matrix failure of whole system is occurrence when parts that belong in (r,s) matrix part procession of parts arranged with procession are breakdown all. The preventive maintenance about system is very important viewing from system reliability and operational expense viewpoint. Preventive maintenance that misses a time calls big loss by system failure and expense of frequent full equipment is paid excessively in preventive maintenance itself but expense is paid much in preventive maintenance itself and whole expense escalation can be achieved preferably. Through this research, reliability model is constructed that do expense by smallest under full equipment policy chosen through comparison of each full equipment policy and preventive maintenance expense full equipment cycle and r ,s value are made using simulated annealing algorithm and simulated annealing algorithm that converge fast in multi-component system certified most suitable to optimization decision

• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3595-3603
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• 2022

Nuclear safety-class DCS is used for nuclear reactor protection function, which is one of the key facilities to ensure nuclear power plant safety, the maintenance for DCS to keep system in a high reliability is significant. In this paper, Nuclear safety-class DCS system developed by the Nuclear Power Institute of China is investigated, the model of reliability estimation considering nuclear power plant emergency trip control process is carried out using Markov transfer process. According to the System-Subgroup-Module hierarchical iteration calculation, the evolution curve of failure probability is established, and the preventive maintenance optimization strategy is constructed combining reliability numerical calculation and periodic overhaul interval of nuclear power plant, which could provide a quantitative basis for the maintenance decision of DCS system.

• Smart Structures and Systems
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• 제15권3호
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• pp.609-625
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• 2015

Management of infrastructure stock is essential in sustainability of society, and its analysis and optimization are studied in the light of control system modeling in this paper. At the first part of the paper, cost of stock management is analyzed based on macroscopic statistics on infrastructure stock and economical growth. Stock management burden relative to economy is observed to become larger at low economic growth periods in developed economies. Then, control system modeling of stock management is introduced and by augmenting maintenance actions as control input, dynamic behavior of stock is simulated and compared with existing time history statistics. Assuming steady state conditions, applicability of the model to cross sectional data is also demonstrated. The proposed model is enhanced so that both preventive and corrective maintenance can be included as system inputs, i.e., feedforward and feedback control inputs. Optimal management strategy to achieve specified deteriorated stock level with minimal cost, expressed in terms of preventive and corrective maintenance actions, is derived based on estimated parameter values for corrosion of steel bridges. Relative cost effectiveness of preventive maintenance is shown when target deteriorated stock level is lower.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.826-828
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• 1996

Genetic Algorithm(GA) is a searching or optimizing algorithm based on natural evolution principle. GA has demonstrated considerable success in providing good solutions to many nonlinear, multi-dimensional optimization problems. The preventative maintenance scheduling is a kind of dynamic optimization problem with constraints. This paper applies GA to the preventative maintenance scheduling problem. In the case study, we can get the preventative maintenance scheduling of 3-generators during 8 weeks using GA. It is shown that GA can be available to the preventative maintenance scheduling problem.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.222-224
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• 2006

This paper addresses a particle swarm optimization-based approach for solving a generating unit maintenance scheduling problem(GMS) with some constraints. We focus on the power system reliability such as reserve ratio better than cost function as the objective function of GMS problem. It is shown that particle swarm optimization-based method is effective in obtaining feasible schedules such as GMS problem related to power system planning and operation. In this paper, we find the optimal solution of the GMS problem within a specific time horizon using particle swarm optimization algorithm. Simple case study with 16-generators system is applicable to the GMS problem. From the result, we can conclude that PSO is enough to look for the optimal solution properly in the generating unit maintenance scheduling problem.

• 한국측량학회지
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• 제20권4호
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• pp.367-374
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• 2002

주요 간선도로의 유지관리는 도로 발전 과정의 계획 단계에서 고려되어야 하는 중요한 문제이다. 많은 도로 유지 관리 시스템들이 좀더 효율적인 의사 결정을 통해 정밀하게 유지보수 비용과 도로포장 비용을 산정 할 수 있도록 개발되었지만 이러한 시스템은 유지관리 비용을 줄이는데 별다른 도움을 주지 못했다. 본 연구에서는 도로 계획 단계에서 도로 설계의 특성들을 최적화함으로써 도로의 설계 수명동안 발생하는 유지보수 관리비용을 감소시켰으며, 절성토면의 경사에 비중을 두고 유지보수 비용을 최소화 할 수 있는 설계변수들에 기초한 초기비용 및 유지보수 비용을 공식화하였다. 도로 유지보수비용은 측면경사와 도로 횡단폭 그리고 연 평균 일일 교통량으로 표현되었다. 적용된 모델지역은 충북 청주-상주간의 실제 지형 데이터베이스를 사용하였으며, 도로 선형 결정시 도로 유지보수 비용과 토질 특성의 민감한 사안들을 조사하기 위해 제시하였다. 결과는 도로 유지보수 비용과 토질 특성은 도로선형 최적화에 중요한 고려사항임을 알 수 있었다.

• 벤처창업연구
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• 제7권4호
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• pp.41-47
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• 2012

철도차량은 사용기간이 길고 철로 위를 달리기 때문에 마모, 진동이 심하여 다른 교통수단에 비해 정비비용이 많이 소요된다. 이에 따라 정비최적화는 매우 중요하다. 본 연구에서는 코레일에서 운용하고 있는 철도차량 정비현황을 분석해 보고 개선방안을 찾고자 하였다. 특히, 차량정비인력이 많이 소요되는 일상검수에 대해 가장 효율적인 정비 주기와 방법을 적용하고자 하였다. 철도차량은 컴퓨터시스템 적용으로 자가진단이 가능하고 과거에 비해 차량의 품질이 많이 향상되었으나, 현행 일상검수는 관례적으로 짧은 기간 시행해야 한다는 측면에서 현재 비효율적인 측면을 시급히 개선해야 함은 당연하다. 따라서 본 연구에서는 차량의 상태를 반영한 개선된 일상검수 주기를 적용하여 차량의 신뢰성 확보와 정비비용을 최소화하고자 한다.

• 한국철도학회논문집
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• 제13권2호
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• pp.222-228
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• 2010

전기 기관차 중수선 시설과 같이 복잡한 시스템의 설계 변수와 중요 변수 최적화는 수리적인 형태로 분석하는 것이 매우 어려운 작업이 된다. 본 논문에서는 메타 모델의 개념을 시뮬레이션 근사 모델에 적용하여 설계 변수와 중요 변수의 최적화를 수행하였다. 시뮬레이션 설계를 위하여 Critical Path 분석과 민감도 분석 수행하여 설계 변수와 실험 횟수를 줄이기 위하여 노력을 하였다. 시뮬레이션 분석은 다 목적 비선형 계획법을 구성한 후 파레토 최적해 집합을 산출하여 설계자에게 다중 대안의 해 집합을 제시하여 실제 시스템의 적용에 대한 유동성을 제공하려고 노력하였다. 본 논문에서 제시 된 기법은 열차 중수선 시설의 설계 및 분석에 있어서 시뮬레이션과 메타 모델을 이용한 하나의 방법으로 이용이 가능 할 것이다.