• 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 Preventive Medicine and Public Health
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• v.35 no.4
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• pp.322-330
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• 2002

Objective: This study was conducted to automatically improve the completeness and validity of the Daegu Cancer Registry, using cross record linkage of many data sources, and to develop a computerized patient enrollment system for efficient communication among cancer researchers via the internet. Method: We analyzed 10,229 cancer patients who were reported in the National Cancer Registry, and from pathological reports, health insurance cancer claims lists, cancer patient records at hospital information centers and death certificates from the Korea National Statistical Office. Result: We confirmed 4,624 cancer patients and found 897 of new cases from a review of medical chart. The new cases were detected efficiently using cross record linkage. We developed a computerized patient enrollment system, based on a client-sewer model, for the input of cancer patients, and then developed a web-based reporting homepage and patient enrollment system for the internet. Conclusion: This system could manage cancer databases systematically, and could be given to other researchers as a basic database.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.592-596
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• 2005

The preventive diagnostic system prevents transformers from power failure by giving alarm and observing transformers in service. And it helps to establish the plan for optimum maintenance of transformer as well as to find location or cause of fault using accumulated data. KEPCO has adopted the preventive diagnostic system at nine 345kV substations since 1997. Techniques for component sensors of preventive diagnostic system were settled but diagnostic algorithm, diagnostic criteria and practical use of accumulated data are not yet established. This paper, to build up the base of preventive diagnostic algorithm for the power transformer, investigated the preventive diagnostic criteria for power transformer.

• Journal of Applied Reliability
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• v.14 no.2
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• pp.122-128
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• 2014

In this paper, we consider the periodic preventive maintenance model for a repairable system following the expiration of extended warranty under replacement-repair warranty. Under the replacement-repair warranty, the failed system is replaced or minimally repaired by the manufacturer at no cost to the user. Also, under extended warranty, the failed system is minimally repaired by the manufacturer at no cost to the user during the original extended warranty period. As a criterion of the optimality, we utilize the expected cost rate per unit time during the life cycle from the user's perspective. And then 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.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.481-488
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• 2009

GIS is used to isolate large power electrical equipment using SF6 gas. While GIS has simple structure, it has few break down, relatively high reliability. But it is hard to check up faults for reason of pressure. Faults of GIS should have a ripple effect on community and be hard to recovery. Consequently, GIS imports a preventive diagnostic system to find internal faults in advance. It is most important that reliability on the GIS preventive diagnostic system, because it estimates abnormality of system by analysis result of collected data. But, exist system which used central data management is low efficiency, and hard to guarantee timeliness and accuracy of data. To guarantee timeliness and accuracy, the GIS preventive diagnostic system needs accordingly to use a real-time middleware. So, in this paper, to improve reliability of the GIS preventive diagnostic system, we use a middleware based on TMO for guaranteeing timeliness of real-time distributed computing. And we propose an improved GIS preventive diagnostic system applying data acquisition, monitoring and control methods based on the TMO model. The presented system uses the Communication Control Unit(CCU) for distributed data handling which is supported by TMO. CCU can improve performance of the GIS preventive diagnostic system by guaranteeing timeliness of data handling process and increasing reliability of data through the TMO middleware. And, it has designed to take full charge of overload on a data acquisition task had been processed in an exist server. So, it could reduce overload of the server and apply distribution environment from now. Therefore, the proposed system can improve performance and reliability of the GIS preventive diagnostic system and contribute to stable operation of GIS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.78-89
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• 2022

The purpose of this study was to propose useful suggestion by analyzing preventive replacement policy under which there are minor and major failure. Here, major failure is defined as the failure of system which causes the system to stop working, however, the minor failure is defined as the situation in which the system is working but there exists inconvenience for the user to experience the degradation of performance. For this purpose, we formulated an expected cost rate as a function of periodic replacement time and the number of system update cycles. Then, using the probability and differentiation theory, we analyzed the cost rate function to find the optimal points for periodic replacement time and the number of system update cycles. Also, we present a numerical example to show how to apply our model to the real and practical situation in which even under the minor failure, the user of system is not willing to replace or repair the system immediately, instead he/she is willing to defer the repair or replacement until the periodic or preventive replacement time. Optimal preventive replacement timing using two variables, which are periodic replacement time and the number of system update cycles, is provided and the effects of those variables on the cost are analyzed.

• 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 Preventive Medicine and Public Health
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• v.28 no.2 s.50
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• pp.450-461
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• 1995

This paper tested by using Micro TSP, an empirical econometric analysis to approve officially a hypothesis of price elasticity of the demand for medical care services in Korean national medical insurance and the economic effect of health care delivery system with time-series datas of Medical Insurance Statistical Yearbook$(1981\sim1993)$. The results suggest that the Korean medical insurance system shows moral hazard due to the change of coinsurance and the economic effect according to intervention of the health care delivery system, but it is different by insurers regardless of the same structure of the medical insurance scheme.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.483-485
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• 2005

In 2003, there was a wide-area blackout in the United States and Canada. More than fifty million people underwent power failure and the estimated financial loss was about four billion dollars. By such wide-area blackouts, the interest in voltage stability has increased gradually. In order to maintain the voltage stability, the preventive control is essential for a contingency. In this paper, a proper preventive control is determined for defined severe contingencies. Among the preventive control methods (generation rescheduling, load curtailment, tap adjusting, injecting the shunt capacitor, and so on.), this paper presents the injection of shunt capacitors by the sensitivity analysis of the voltage stability assessment for preventive controls. The 2006-2010 KEPCO summer peak system is used in case studies.

• 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.