• Journal of the military operations research society of Korea
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• v.15 no.1
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• pp.54-62
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• 1989

This paper presents a corrective maintenance model to determine either type of maintenance actions upon failure of the system. Types of maintenance actions considered are minimal repair and replacement. Minimal repair cost is assumed to be random, whereas replacement cost is fixed. A policy, B(t), which determines the type of maintenance action based on the estimated minimal repair cost when the system fails at time t is adopted. To obtain an optimal policy, an expected maintenance cost per unit time is derived and is minimized with respect to B(t).

• 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.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1989-1992
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• 2009

Maintenance is classified preventive maintenance before performing equipment failure and corrective maintenance after performing equipment failure. In preventive maintenance, we may analyze the failure data to end from beginning of equipment and allocate maintenance method and calculate maintenance cycle quantitatively by the failure data analysis. So, it has a merit to reduce system maintenance cost and to operate effectively but, it require high cost in system introducing and continuous operation to end of system. In corrective maintenance, we may calculate MTTR(mean time to repair) quantitatively based on function failure time. it can be based on establishing maintenance system for operation efficiency. In this paper, we may reflect the MTTR for the onboard equipped in Tilting train to establish maintenance system for Tilting train operation efficiency.

• Journal of the Korean Society for Railway
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• v.12 no.1
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• pp.88-94
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• 2009

Life cycle costing is one of the most effective approaches for the cost analysis of long-life products such as the railroad vehicle. Life cycle costing includes the cost of concept design, development, manufacture, operation, maintenance and disposal. Especially, life cycle costing in the railroad industry has been focused on the maintenance cost. In this paper, the standard, guide and maintenance information of railroad vehicle were investigated, and the unique corrective and preventive maintenance templates of railroad vehicle were proposed. Maintenance cost of an auxiliary power supply system of EMU was predicted by using the proposed templates. The results show that the preventive maintenance, PM, cost is much higher compare to corrective maintenance, CM, cost because of daily and monthly maintenance tasks which require lots of labor work. It is expected that these templates can help railroad operators make maintenance strategies with consideration of the cost parameter.

• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.2
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• pp.87-91
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• 1989

A warranty is a contractual obligation incurred by a producer in connection with the sale of a product. The warranty specifies that producer agrees to remedy certain failures in the product sold. There have been many articles dealing with warranties, but they have studied about optimal warranty cost for the warranty period. In this study, an optimal preventive maintenance time interval is computed. The optimal preventive maintenance time interval minimizing warranty cost for the warranty period is discussed. It is assumed that failure rate is increasing and the failure rate after preventive maintenance or corrective maintenance lies between good as new and bad as old.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.6
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• pp.785-793
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• 1999

There are two kinds of method in ship maintenance. One is the corrective maintenance and the other is the preventive maintenance. For these maintenances recently the stochastic techniques are widely used to keep the maximum availibility and the optimal maintenance period minimizing a given cost function. Thus this paper suggest a method to decide the optimal policy of ship's maintenances by using dynamic programming and the effectiveness of the method is verified through several examples in which failure rates and maintenance data of ship's machineries and parts are given.

• Journal of Applied Reliability
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• v.16 no.4
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• pp.280-286
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• 2016

Purpose: Recently, Jiang defines the tradeoff B life to minimize a sum of life lost by preventive maintenance (PM) and corrective maintenance (CM) contribution parts and sets up an optimal replacement age of age replacement policy as this tradeoff life. In this paper, Jiang's model only considering the known lifetime distribution is extended by assigning different weights to two parts of PM and CM in order to reflect the practical maintenance situations in application. Methods: The new age replacement model is formulated and the meaning of a weight factor is expressed with the implied cost of failure under asymptotic expected cost model and also discussed with one-cycle expected cost criterion. Results: The proposed model is applied to Weibull and lognormal lifetime distributions and optimum PM replacement ages are derived with corresponding implied cost of failure. Conclusion: The new age replacement policy to escape the estimation of cost of failure in classical asymptotic expected cost criterion based on the renewal process is provided.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1202-1207
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• 2011

This article introduces the benchmarking methodologies, the results of comparative analysis on the railway electricity maintenance management - preventive maintenance, corrective maintenance, upgrade maintenance. This article also compares the Korean railway electricity maintenance management with the results mentioned above and analyses the situations of each country - France, Germany, Japan. Lastly this article tries to find the comparative position of the Korean railway infrastructure costs and discusses the direction of cost reduction in railway electricity maintenance management.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.35
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• pp.9-16
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• 1995

The degeneration of tool in material removal processing machinery can be characterized by wear, deflection, chattering and any failure in tool or in the material In be processed. In the previous studies, first three of them are analyzed as a preventive maintenance strategy in quality control area. The last of them, any failure, is analyzed as a preventive maintenance strategy in reliability area. In this research, we propose a simple integrated mathematical model which minimizes the cost of machinery failures and producing defects. We determine the optimal wear limit of tool by considering the percent defects. cost, the preventive maintenance cost, and the corrective maintenance cost.