• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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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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• v.54 no.10
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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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• v.15 no.3
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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.

• Proceedings of the KIEE Conference
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• 1996.07b
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.4
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• pp.367-374
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• 2002

Maintainability of highways is an important problem that is considered in the planning steps of a highway development process. A number of asset management systems have been developed to precisely predict maintenance and pavement expenditures for better decision making, But these systems are not helpful in reducing maintenance costs. Optimization of some highway design characteristics in the planning phases may reduce maintenance costs over the life cycle of highway. The formulations for initial and maintenance costs have been developed based on which design variables can be chosen to minimize these costs, focusing on the sideslope in cut and fill sections. Maintenance cost has been represented as a function of sideslope, width of highway cross section, and annual average daily traffic. A real geographic database of between Chung joo and Sang joo city in Choong buk was used and it is presented to investigate the sensitivities of maintenance cost and soil characteristics in selecting alignments. In this study, we present that maintenance cost and soil characteristics are important considerations in alignment optimization.

• Proceedings of the KIEE Conference
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• 2006.11a
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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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.4
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• pp.41-47
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• 2012

Railroad rolling stock has long service life and a lot of maintenance cost running on rail by wear and vibration. And it is very important to get optimization of maintenance. This paper want to analyze rolling stock maintenance situation of KORAIL and find out its improvement methods. Especially, the purpose of this paper is to adopt the most effective maintenance period and methods to daily inspection which needs many maintenance manpower in rolling stock. Rolling stock has self-diagnosis function using computer system and the quality of rolling stock has much improved these days but current daily inspection repeat for short period routinely and it is very ineffective. Therefore, the paper adopt improved daily inspection period reflecting the characteristics of rolling stock, and want to secure reliability of rolling stock and minimize maintenance cost.

• Journal of the Korean Society for Railway
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• v.13 no.2
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• pp.222-228
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• 2010

In this paper, we propose a optimization approach for the Electric Locomotive Overhaul Maintenance Facility (ELOMF), which aims at the simulation optimization so as to meet the design specification. In simulation design, we consider the critical path and sensitivity analysis of the critical (dependent) factors and the design (independent) parameters for the parameter selection and reduction of the metamodel. Therefore, we construct the multi-objective non-linear programming. The objective function is normalized for the generalization of design parameter while the constraints are composed of the simulation-based regression metamodel for the critical factors and design factor's domain. Then the effective solution procedure based on the pareto optimal solution set is proposed. This approach provides a comprehensive approach for the optimization of Train Overhaul Maintenance Facility(TOMF)'s design parameters using the simulation and metamoels.