• Journal of the Korean Society for Railway
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• v.16 no.3
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• pp.196-201
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• 2013

As there is no failure data for the entire lifecycle of a product, when analyzing reliability measures based on early failure data only, there may be a significant error between the estimated mean life and the real one, because it can be underestimated, or on the other hand, it can be overestimated when analyzing reliability measures based on a large amount of censored data with the failure data. To resolve the issue, this study proposes an optimal sampling estimation procedure that selects the proportion of censored data to estimate the optimal distribution with the idea that the estimated distribution could be approximated as closely as the real life distribution. This would work if we sampled the optimal proportion on the censored data, because failure data has real intrinsic distribution in any situation. We validate the proposed procedure using an actual example. If the proposed method is applied to the maintenance policy of TWC (Train to Wayside Communication) system, then we can establish the optimal maintenance policy. Thus, we expect that it will be effective for improvement of reliability and cost savings.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8700-8706
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• 2015

The first driving device of the power bogies for the Korean high-speed railway vehicle consists of the traction motor (TM) and the motor reduction gears unit (MRU). Although TM and MRU are the mechanically integrated structures, their time between overhauls (TBO) have two separate intervals due to different technical requirements(i.e. TBO of MRU: $1.8{\times}10^6km$, TBO of TM: $2.5{\times}10^6km$). Therefore, to reduce the unnecessary number of preventive maintenances, it is important to evaluate the optimal TBO with a viewpoint of reliability-center maintenance towards cost-effective solution. In this study, derived from the field data in maintenance, fault tree analysis and failure rate of the subsystem considering criticality of the components are evaluated respectively. To minimize the conventional total maintenance cost, the same optimal TBO of the components is derived from genetic algorithm considering target reliability and improvement factor. In this algorithm, a chromosome which comprised of each individual is the minimum preventive maintenance interval. The fitness function of the individual in generation is acquired through the formulation using an inverse number of the total maintenance cost. Whereas the lowest common multiple method produces only a four percent reduction compared to what the existing method did, the optimal TBO of them using genetic algorithm is $2.25{\times}10^6$km, which is reduced to about 14% comparing the conventional method.

• Journal of Korean Society for Quality Management
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• v.18 no.2
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• pp.33-42
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• 1990

This paper proposes the maintenance model of multi-component system when the failure characteristics and types of components are considered. In this model, it is assumed that a system is composed of a critical component, a major component and a minor component. Also, failure types is classified into major failure and minor failure. If major failure occurs to critical component before system age replacement time, the system is renewed. If major failure does not occur until its age replacement time, preventive maintenance is performed at age replacement time T. Minimal repairs are carried out after each minor failure. Major component is minimal-repaired if any failure is discovered during operation. Minor component should be replaced as soon as any failure is found. This paper determines the optimal replacement time of the system which minimizes total maintenance cost. Numerical example illustrates these results.

• 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.

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.109-120
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• 1993

Most systems are composed of components which have different failure chracteristics. Since the failure characteristics of components is different, it is rational and reasonable to establish a maintenance model to be considered repair and replacement policies which are proper to failure characteristics of these components. This paper proposes the age replacement time for a system composed of components which have different failure characteristics. In this model, it is assumed that a system is composed of a critical failure component, a major failure component, minor failure component. If any failure occurs to critical component before its age replacement time, the system should be replaced. If any failure does not occur until its age replacement time, preventive replacement should be performed at age replacement time T. Major component is minimal repaired if any failure occurs during operation. Minor component should be replaced as soon as failure is found. This paper determines the optimal replacement time of the system which minimize, total maintenance cost and initial stock Quantity of minor component within this optimal replacement time. Numerical example illustrates these results.

• Management Science and Financial Engineering
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• v.3 no.1
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• pp.57-74
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• 1997

An EOQ-like inventory model for a manufacturing process is studied. The system is assumed to deteriorate during the production process. The results are either the production of a number of defective items, or the breakdown of the production machine. The optimal production lot size is derived. The model is extended to the case in which the probabilities of making defective items and machine breakdowns are a function of both the quantity (amount) and quality (performance) of the consumed setup cost (including the preventive maintenance cost). We further assume that the setup performance can be improved by investing in the performance improvement program. Hence, the same or a better setup outcome can be achieved with a lower setup cost. We then investigate the optimal setup cost and investment policy simultaneously, thereby achieving a better process quality and setup cost reduction concurrently.

• Journal of the Korea Safety Management & Science
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• v.10 no.3
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• pp.89-98
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• 2008

A preventive maintenance model, caller FNBM($\alpha$, $\delta$, $\gamma$)model, is proposed to decide an optimal repair number under achieved availability requirements(r) along with taking two types of failures (repairable or irrepairable) into account. In this model, the current system is replaced by a new one in case when it doesn't meet the achieved availability requirement, even though it is repairable failure; Otherwise it is replaced in time of the first irrepairable failure. Assumed that the j-th failure is repairable with probability ${\alpha}_j$ minimal repairs are allowed for repairable failure between replacements. Expected cost rate for preventive maintenance model is developed using NHPP(Non-Homogeneous Poisson Process) in order to determine the optimal number $n^*$, also numerical examples are shown in order to explain the proposed model. Since the proposed FNBM($\alpha$, $\delta$, $\gamma$)model includes Park FNBM model(1979) and Nakagawa FNBM(p)model(1983) this proposed model is thought to be better than previous model, especially for weapon system which requires availability as primary parameter.

• IE interfaces
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• v.15 no.1
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• pp.89-98
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• 2002

This paper considers the optimum replacement times of a steam generator in nuclear power plant with failure data. It is assumed that the failure pattern of units is given as a Weibull distribution and repair and periodic preventive maintenance are performed periodically. The maximum likelihood method is used to estimated the Weibull parameters of failure distribution from failure data. Relpacement, output-decresing and maintenance costs are considered to determine the optimal replacement times by simulation. Numerical examples are included with actual failure data and cost estimators.

• Proceedings of the Korean Reliability Society Conference
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• 2005.06a
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• pp.121-129
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• 2005

In this paper, we consider a periodic preventive maintenance(PM) policy in which each PM reduces the hazard rate of amount proportional to the failure intensity, which increases since the last PM and slows down the wear-out speed to that of new one. And the proportion of reduction in hazard rate decreases with the number of PMs. Our model is similar to $ARI_1$ proposed by Doyen and Gaudoin(2004) in the sense of reduction of hazard rate. Our model has totally different wear-out pattern of hazard rate after PM's, however, and the proportion of reduction depends on the number of PM's. Assuming that the system undergoes only minimal repairs at failures between PM's, the expected cost rate per unit time is obtained. The optimal number N of PM and the optimal period x, which minimize the expected cost rate per unit time are discussed. Explicit solutions for the optimal periodic PM are given for the Weibull distribution case.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.53-59
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• 2021

The marine transport industry generally applies new technologies later than other transport industries, such as airways and railways. Vessels require efficient operation, and their performance and lifespan depend on the level of maintenance and management. Many studies have shown that corrective maintenance (CM) and time-based maintenance (TBM) have restrictions with respect to enabling efficient maintenance of workload and cost to improve operational efficiency. Predictive maintenance (PdM) is an advanced technology that allows monitoring the condition and performance of a target machine to predict its time of failure and helps maintain the key machinery in optimal working conditions at all times. This study presents the development of a marine predictive maintenance (MPdM; maritime predictive maintenance) method based on applying PdM to the marine environment. The MPdM scheme is designed by considering the special environment of the marine transport industry and the extreme marine conditions. Further, results of the study elaborates upon the concept of MPdM and its necessity to advancing marine transportation in the future.