• Journal of Korean Institute of Industrial Engineers
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• v.36 no.2
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• pp.108-116
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• 2010

The maintenance of a deteriorating system is often imperfect. Previous studies have shown that the imperfect preventive maintenance (PM) can reduce the wear out and aging effects of deteriorating systems to a certain level between the conditions of as good as new and as bad as old. In this paper, we employ the concept of the improvement factor in investigating two optimal PM policies; failure limit policy and periodic PM policy. We redefine the improvement factor model as a function of the cost of PM, using this concept, we derive the conditions of optimal PM policies and formulate expressions to compute the expected cost rate. Based on this information, the determination of the maintenance policies which minimize the cost rate is examined. Numerical examples for the Weibull distribution case are also given.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.263-271
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• 1997

This paper presents a preventive maintenance model for determining the preventive replacement period of a system in which a failure rate is affected by the cumulative damage of fault and inspection. Especially, the failure rate function is considered to be a function of the cumulative damage of the fault and inspection time. Types of replacement considered are preventive replacement and failure replacement. Failure rate and expected cost function between replacement are derived. An optimal policy is obtained that minimizes the average cost per unit time for preventive replacement, failure replacement, inspection and repair.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.773-781
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• 2013

Recently, an extended warranty of the system following the expiration of the basic warranty is becoming increasingly popular to the user. In this respect, we suggest a preventive maintenance model following the expiration of extended warranty with minimal repair warranty from the user's point of view in this paper. Under basic warranty and extended warranty, the failed system is minimally repaired by the manufacturer at no cost to the user. For the preventive maintenance model, we derive the expressions for the expected cycle length, the expected total cost and the expected cost rate per unit time. Also, we determine the optimal preventive maintenance period and the optimal preventive maintenance number by minimizing the expected cost rate per unit time. Finally, the numerical examples are presented to illustrate the purpose when the failure time of the system has a Weibull distribution.

• Proceedings of the Korean Reliability Society Conference
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• 2005.06a
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• pp.115-120
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• 2005

This paper introduces models for preventive maintenance policies and considers periodic preventive maintenance policy with minimal repair when the failure of system occurs. It is assumed that minimal repairs do not change the failure rate of the system. The failure rate under prevention maintenance received an effect by a previously prevention maintenance and the slope of failure rate increases the model where it considered. Also the start point of failure rate under prevention maintenance considers the degradation of system and that it increases quotient, it assumed. Per unit time it bought an expectation cost from under this prevention maintenance policy. We obtain the optimal period time and the number for the periodic preventive maintenance by using Nakagawa's Algorithm, which minimizes the expected cost rate per unit time. Finally, it suppose that the failure time of a system has a Weibull distribution as an example and we obtain an expected cost rate per unit time the optimal period time and the number when cost of replacement and cost of minimal repair change.

• 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.

• Journal of Korean Society for Quality Management
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• v.29 no.3
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• pp.39-48
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• 2001

Preventive maintenance(PM) is an action taken on a repairable system while it is still operating, which needs to be carried out in order to keep the system at the desired level of successful operation. In this paper, we consider a Bayesian approach to determine an optimal periodic preventive maintenance policy. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established. Some numerical examples are presented for illustrative purpose.

• Journal of Korean Institute of Industrial Engineers
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• v.8 no.2
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• pp.33-36
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• 1982

This paper considers preventive maintenance policies for a system with two types of units which is subject to deterioration. Two generalized models are investigated ; a preventive maintenance policy based on the cumulative operating time and a policy based on the number of minimal repairs performed. Optimal preventive maintenance policies which minimize the expected average cost per unit time including the earning loss due to the deterioration are discussed and some numerical examples are given.

• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.104-111
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• 2023

Rolling stock maintenance, which focuses on preventive maintenance, is typically implemented considering the potential harm that may be inflicted to passengers in the event of failure. The cost of preventive maintenance throughout the life cycle of a rolling stock is 60%-75% of the initial purchase cost. Therefore, ensuring stability and reducing maintenance costs are essential in terms of economy. In particular, private railroad operators must reduce government support budget by effectively utilizing railroad resources and reducing maintenance costs. Accordingly, this study analyzes the reliability characteristics of components using field data. Moreover, it resolves the problem of determining an economical replacement interval considering the timing of scrapping railroad vehicles. The procedure for determining the optimal replacement interval involves five steps. According to the decision model, the optimal replacement interval for the onboard signal device components of the "A" line train is calculated using field data, such as failure data, preventive maintenance cost, and failure maintenance cost. The field data analysis indicates that the mileage meter is 9 years, which is less than the designed durability of 15 years. Furthermore, a life cycle in which the phase signal has few failures is found to be the same as the actual durability of 15 years.

• 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.17 no.3
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• pp.246-255
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• 2017

Purpose: This study proposes the optimal PM (preventive maintenance) policy of leased equipment for lessee's decision-making using two types of reliability condition. Methods: We consider reliability threshold based PM model. Equipment reliability is estimated and used as condition variable. The effect of repair for maintenance is imperfect and represented by age reduction factor. Results: We provide two PM policies. Policy 1 is focused on minimized total cost. This policy guarantees reliability threshold until last maintenance action. Policy 2 focus on maintaining reliability threshold during leased period. The proposed approach provides optimal reliability threshold under number of PM. Through result, we finally construct decision-making process for lessee using reliability threshold and end of reliability. Conclusion: This study provides two PM policy for lessee's decision-making. Through numerical example, we get a result of optimal reliability threshold, number of PM, optimum alternative under lessee's reliability condition.