• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.651-656
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• 2006

Wu and Clements-Croome (2005) investigate the optimization problem of PM policies for situations where the quality of PM is a random variable with a certain probability distribution. However, they assume that the cost of preventive maintenance is constant, not depending on the quality of PM. Thus, this paper considers a periodic PM model when PM cost depends on the quality of PM activity. The optimal PM policy are presented for the extended PM model and the numerical examples are presented for illustrative purpose.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.909-923
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• 2008

This paper considers the replacement model and the preventive maintenance model following the expiration of combination warranty for a repairable system. If the system fails after the combination warranty is expired, then it is minimally repaired at each failure. The criterion used to determine the optimal replacement policy and the optimal preventive maintenance policy is the overall value function based on the expected cost rate per unit time and the expected downtime per unit time. The numerical examples are presented for illustrative purpose when the failure time follows a Weibull distribution.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.147-151
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• 2002

Burn-in is a widely used method to eliminate 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 the Korean Data and Information Science Society
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• v.18 no.3
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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.

• IE interfaces
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• v.16 no.1
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• pp.27-33
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• 2003

The maintenance cost in K Steelworks has been continuously increased in proportion to the production cost. However, there seems to be a possibility of reducing cost through the optimization of maintenance actions. The failure types of the equipment in steelworks ate various with different failure cost. Thus the failure rate and cost of each type of failures should be considered simultaneously when the optimum maintenance period is to be determined. It is considered that the equipment undergoes periodic replacement and a specified number of incomplete preventive maintenance actions are performed during a replacement period. Assuming that the time to failure follows a Weibull distribution, the parameters of the failure rate are estimated using the maximum likelihood estimation. The optimal replacement period is determined to minimize the average cost per unit time. As the result of analysis it is suggested that the existing maintenance period for a hot-rolling equipment can be extended significantly.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.77-86
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• 2008

This paper develops the optimal periodic preventive maintenance policy following the expiration of non-renewing warranty. We assume that Wu and Clements-Croome's (2005) periodic PM model with random maintenance quality is utilized to maintain the system after the non-renewing warranty is expired. Given the cost structure to the user during the cycle of the product, we drive the expressions for the expected cost rate per unit time. Also, we obtain the optimal number and the optimal period by minimizing the expected cost rate per unit time. The numerical examples are presented for illustrative purpose.

• 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.

• Journal of the Korean Society for Railway
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• v.8 no.6 s.31
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• pp.544-549
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• 2005

Nowadays, most of bridge networks are complete or close to completion. The biggest challenge railroad./highway agencies and departments of transportation face is the maintenance of these networks, keeping them safe and serviceable, with limited funds. To maintain the bridges effectively, there is an urgent need to predict their remaining life from a system reliability viewpoint. And, it is necessary to develop the maintenance models based on system reliability concept. In this paper, maintenance models are developed for preventive maintenance and essential maintenance by using system reliability and lifetime distributions. The proposed model is applied to an existing railroad bridge. The optimal maintenance strategy of this bridge is obtained in terms of services life extension and cumulative maintenance cost.

• Journal of Korean Society for Quality Management
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• v.29 no.2
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• pp.46-53
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• 2001

In this paper, a preventive maintenance(PM) policy for a repairable system is considered. The failure rate model proposed by Park et at.(2000) is generalized by assuming that after each PM not only the PM slows down the degradation process of the system but also reduces down the system failure rate by a certain fixed amount. Long-run expected cost rate of the PM policy is derived and the properties of joint solution of the optimal PM period and optimal number of PM which minimizes the expected cost rate are obtained. Numerical examples for the case of a Weibull-type failure rate are given.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.191-204
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• 2006

This paper develops a Bayesian method to derive the optimal sequential preventive maintenance(PM) policy by determining the PM schedules which minimize the mean cost rate. Such PM schedules are derived based on a general sequential imperfect PM model proposed by Lin, Zuo and Yam(2000) and may have unequal length of PM intervals. To apply the Bayesian approach in this problem, we assume that the failure times follow a Weibull distribution and consider some appropriate prior distributions for the scale and shape parameters of the Weibull model. The solution is proved to be finite and unique under some mild conditions. Numerical examples for the proposed optimal sequential PM policy are presented for illustrative purposes.