• 품질경영학회지
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• 제35권4호
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• pp.140-146
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• 2007

In this paper, we consider a periodic imperfect preventive maintenance(PM) policy in which the system's failure rate after each PM remains unchanged. The system undergoes only minimal repairs at failures between PMs. Exact mathematical formula of the expected cost rate per unit time is derived. Optimal number of PMs and optimal maintenance period are derived by minimizing the expected cost rate per unit time. A numerical example is provided to illustrate the proposed approach under Weibull lifetime distribution.

• 한국신뢰성학회지:신뢰성응용연구
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• 제17권3호
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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.

• International Journal of Reliability and Applications
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• 제9권1호
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• pp.113-122
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• 2008

In this paper, we consider a periodic preventive maintenance policy in which each preventive maintenance reduces the hazard rate of amount proportional to the failure intensity, which increases since the system started to operate. And the effect of preventive maintenance at each preventive maintenance epoch is different. The expected cost rate per unit time for the proposed model is obtained. We discuss the optimal number N of the periodic preventive maintenance and the optimal period x, which minimize the expected cost rate per unit time and obtain the optimal preventive maintenance schedule for given cost structures of the model. A numerical example is given for the purpose of illustrating our results when the failure time distribution is Weibull distribution.

• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.587-596
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• 2008

This paper develops the optimal periodic preventive maintenance policies following the expiration of warranty: renewing warranty and non-renewing warranty. After the warranty period is expired, the system undergoes the PM periodically and is minimally repaired at each failure between two successive PMs. Firstly, we determine the expected cost rate per unit time and the expected downtime per unit time for the periodic PM model. Then the overall value function suggested by Jiang and Ji(2002) is applied to obtain the optimal PM period and the optimal PM number. Finally, the numerical examples are presented for illustrative purpose.

• International Journal of Reliability and Applications
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• 제11권2호
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• pp.123-138
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• 2010

This paper investigates a mathematical model of a system composed of two non-identical unit parallel system with common-cause failure, critical human error, non-critical human error, preventive maintenance and two type of repair, i.e. cheaper and costlier. This system goes for preventive maintenance at random epochs. We assume that the failure, repair and maintenance times are independent random variables. The failure rates, repair rates and preventive maintenance rate are constant for each unit. The system is analyzed by using the graphical evaluation and review technique (GERT) to obtain various related measures and we study the effect of the preventive maintenance preventive maintenance on the system performance. Certain important results have been derived as special cases. The plots for the mean time to system failure and the steady-state availability A(${\infty}$) of the system are drawn for different parametric values.

• 품질경영학회지
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• 제29권3호
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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.

• Industrial Engineering and Management Systems
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• 제7권2호
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• pp.113-125
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• 2008

This paper presents a model for designing cellular manufacturing systems (CMS) by integrating system cost, machine reliability, and preventive maintenance (PM) planning. In a CMS, a part is processed using alternative process routes, each consisting of a sequence of visits to machines. Thus, a level of 'system reliability' is associated with the machines along the process route assigned to a part type. Assuming machine reliabilities to follow the Weibull distribution, the model assigns the machines to cells, and selects, for each part type, a process route which maximizes the overall system reliability and minimizes the total costs of manufacturing operations, machine underutilization, and inter-cell material handling. The model also incorporates a reliability based PM plan and an algorithm to implement the plan. The algorithm determines effective PM intervals for the CMS machines based on a group maintenance policy and thus minimizes the maintenance costs subject to acceptable machine reliability thresholds. The model is a large mixed integer linear program, and is solved using LINGO. The results point out that integrating PM in the CMS design improves the overall system reliability markedly, and reduces the total costs significantly.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.689-696
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• 2007

This paper considers a Bayesian approach to determine an optimal PM policy with random maintenance quality. Thus, we assume that the quality of a PM action is a random variable following a probability distribution. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal PM policy. Finally, the numerical examples are presented for illustrative purpose.

• 전기학회논문지
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• 제57권5호
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• pp.735-740
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• 2008

Recently, the research of PM (Preventive Maintenance) method on the RCM(Reliability-Centered Maintenance) of the system equipment is being actively advanced for a few years. For the most of the current power equipment maintenance, the state of the equipment after maintenance is assumed to be becoming 'as good as new ones' state. However, the state of the power equipment is maintained like the states of the between 'as good as new ones' and 'as bad as old ones' by imperfect maintenance that implies the life decrease of the equipment by frequent breakdown, the error of maintenance process, and so on. So, the Maintenance method considering the real case has to reflect Imperfect maintenance than perfect maintenance. This paper suggests the Preventive Maintenance method by using Quasi - Renewal Theory for the gas turbine equipment as deliberating the imperfect maintenance for the real cases.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.193-196
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• 1997

In this paper we consider a Bayesian theoretic approach to periodic incomplete preventive maintenance with minimal repair at failure. We assume that the system failure rate is increasing as the frequency of PM increases and that the system is replaced at the K-th PM under this maintenance strategy. The optimal policies which minimize the expected cost rates are discussed. We seek the optimal periodic PM interval x and replacement time K under a Weibull failure intensity. Assuming suitable prior distribution for the Weibull parameters, we derive the posterior distribution incorporating failure data and obtain the updated optimal replacement strategies.