• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.41-52
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• 2020

This paper investigates the stress-reducing preventive maintenance model through numerical experiments. The preventive maintenance model is used to analyze the relationship between related conditions and variables to gain insight into the efficient operation of the system when performing preventive maintenance in real-world situations. Various preventive maintenance models have been developed over the past decades and their complexity has increased in recent years. Increasing complexity is essential to reflect reality, but recent models can only be interpreted through numerical experiments. The stress-reducing preventive maintenance is a newly introduced preventive maintenance concept and can only be interpreted numerically due to its complexity, and has received little attention because the concept is unfamiliar. Therefore, for information purposes, this paper investigates the characteristics of the stress-reducing preventive maintenance and the relationship between parameters and variables through numerical experiments. In particular, this paper is focusing on the economic feasibility of stress-reducing preventive maintenance by observing changes in the optimal preventive maintenance period in response to changes in environmental stress and the improvement factor. As a result, when either the environmental stress or the improve effect of stress-reducing preventive maintenance is low, it is not necessary to carry out the stress-reducing preventive maintenance at excessive cost. In addition, it was found that the age reduction model is more economical than the failure rate reduction model.

• Journal of Applied Reliability
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• v.12 no.2
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• pp.57-66
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• 2012

In this paper, we consider the periodic preventive maintenance model for a repairable system following the expiration of replacement-repair warranty. Under this 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 optimal periodic preventive maintenance policy is given for Weibull distribution case.

• International Journal of Reliability and Applications
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• v.9 no.1
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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 Applied Reliability
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• v.18 no.2
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• pp.104-113
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• 2018

Purpose: This paper proposes the non-periodic preventive maintenance policy based on the level of cumulative hazard intensity. We aim to construct a cost-effectiveness on the proposed model with relaxing the constraint on reliability. Methods: We use the level of cumulative hazard intensity as a condition variable, instead of reliability. Such a level of cumulative hazard intensity can derive the reliability which decreases as the frequency of preventive maintenance action increases. We also model the imperfect preventive maintenance action using the proportional age setback model. Conclusion: We provide a numerical example to illustrate the proposed model. We also analyze how the parameters of our model affect the optimal preventive maintenance policy. The results show that as long as high reliability is guaranteed, the inefficient preventive maintenance action is performed reducing the system operation time. Moreover, the optimal value of the proposed model is sensitive to changes in preventive maintenance cost and replacement cost.

• International Journal of Reliability and Applications
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• v.11 no.2
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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.

• IE interfaces
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• v.15 no.3
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• pp.286-296
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• 2002

Products which are sold with warranty, preventive maintenance actions by manufacturers and/or buyers have an impact on the total costs for both parties. In this paper, we develop the models to study the expected warranty cost for products with free repairable warranty with three types of discrete preventive maintenance. We deal with by utilizing the concept that preventive maintenance reduces the virtual age of the system. We assume that the maintenance planning horizon can be segmented into k discrete and equally sized periods. In such a scenario, numerical examples are presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.24-31
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• 2018

As a system complexity increases and technology innovation progresses rapidly, leasing the equipment is considered as an important issue in many engineering areas. In practice, many engineering fields lease the equipment because it is an economical way to lease the equipment rather than to own the equipment. In addition, as the maintenance actions for the equipment are costly and need a specialist, the lessor is responsible for the maintenance actions in most leased contract. Hence, the lessor should establish the optimal maintenance strategy to minimize the maintenance cost. This paper proposes two periodic preventive maintenance policies for the leased equipment. The preventive maintenance action of policy 1 is performed with a periodic interval, in which their intervals are the same until the end of lease period. The other policy is to determine the periodic preventive maintenance interval minimizing total maintenance cost during the lease period. In addition, this paper presents two decision-making models to determine the preventive maintenance strategy for leased equipment based on the lessor's preference between the maintenance cost and the reliability at the end of lease period. The structural properties of the proposed decision-making model are investigated and algorithms to search the optimal maintenance policy that are satisfied by the lessor are provided. A numerical example is provided to illustrate the proposed model. The results show that a maintenance policy minimizing the maintenance cost is selected as a reasonable decision as the lease term becomes shorter. Moreover, the frequent preventive maintenance actions are performed when the minimal repair cost is higher than the preventive maintenance cost, resulting in higher maintenance cost.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.869-874
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• 2019

Performing preventive maintenance on a system reduces unexpected downtime caused by system aging and increases its availability. In general, preventive maintenance can be largely divided into two broad categories: time-based maintenance policy and condition-based maintenance policy. In the time-based maintenance policy the preventive maintenance is triggered at scheduled time epochs with fixed time intervals, while in the condition-based maintenance policy the preventive maintenance is performed when system state is checked to satisfy a specific condition. Condition-based maintenance has some benefits in improving maintenance efficiency, compared to time-based one. This paper presents a stochastic model for analyzing a system with condition-based preventive maintenance, where the preventive maintenance is performed after a random time since the system aging occurs, and provides an analytical solution for the steady-state availability and the corresponding profit.

• Journal of Applied Reliability
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• v.11 no.2
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• pp.167-176
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• 2011

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 periodic time and the number for the periodic preventive maintenance by using Nakagawa's Algorithm, which minimizes the expected cost per unit time.

• Journal of Applied Reliability
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• v.16 no.4
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• pp.313-322
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• 2016

Purpose: We introduce ways to employ Markov chain model to evaluate the effect of preventive maintenance process. While the preventive maintenance process decreases the failure rate of each subsystems, it increases the downtime of the system because the system can not work during the maintenance process. The goal of this paper is to introduce ways to analyze this trade-off. Methods: Markov chain models are employed. We derive the availability of the system consisting of N repairable subsystems by the methods under various maintenance policies. Results: To validate our methods, we apply our models to the real maintenance data reports of military truck. The error between the model and the data was about 1%. Conclusion: The models developed in this paper fit real data well. These techniques can be applied to calculate the availability under various preventive maintenance policies.