• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.9-11
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• 2006

This paper suggests a revision method for historical fault data using Proportional Aging Reduction(PAR) to consider maintenance effect in time-varying failure rate. In order to product time-varying failure rate, the historical fault data are necessary. However, the maintenance record could be left out in historical data by spot operator's mistake. In this case, the failure rate is produced less than the average failure rate for increasing equipments' life-time by maintenance effect. Hence, it is necessary for new time-varying failure rate to extract maintenance effect from the existing fault data. In this paper, the revision method to reduce equipments' life-time, adversely using PAR among three techniques to consider maintenance effect.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.495-504
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• 2009

As the Reliability Centered Maintenance(RCM) is being studied, maintenance tasks can be performed effectively through the Risk Priority Number(RPN) evaluation about the components in the system. The RPN is usually calculated through arithmetical operations of three values, Severity, Occurrence, and Detection for each facility. This RPN provides information that includes risk level of the facility and the priority order of maintenance tasks for facility. However, if there is no sufficient historical failure data, it is difficult to calculate the RPN. In this case, historical failure data from other sources can be used and apply this data to korean railway system. In this paper, it is proposed that a new methodology to model the failure rate as a fuzzy membership function. This method is based on failure data from other sources by means of the fuzzy theory and the expert opinion system. And considering assessment tendency of each expert, distortions that happened when the failure rate of facilities is estimated were minimized. This results determine Occurrence values of facilities. Taking advantage of this result., the RPN can be calculated with Severity and Detection of facilities by using the fuzzy operation. The proposed method is applied the rail-way power substation.

• Journal of the Korean Society for Railway
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• v.12 no.6
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• pp.921-926
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• 2009

The RPN provides information which includes the risk level and the priority order of maintenance tasks for components. However, if there is no sufficient historical failure data, the historical failure data from other sources can be applied to the target system. And if we use historical data from other sources without any process, there will be concomitant problems according to a discord of each system characteristic, a difference between the present and the date of failure data, etc. In this paper, a new methodology is proposed to model the failure rate as a fuzzy function to resolve these problems. Taking advantage of this result, the RPN can be calculated by using the fuzzy operation. The proposed method is applied to the substation system.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.79-86
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• 2020

Failure data that has systematically managed for a long time has high reliability to an estimated volume. But since much cost and effort are needed to secure reliability data, data from overseas country is used in quantitative risk analysis in many workplaces. Reliability of the data that can be collected in workplaces can be dropped because of insufficient sample or lack of observation time. Therefore, estimated data is difficult to use as it is and environment and characteristic of the workplace cannot be reflected by using data from overseas country. So this study used Bayesian method that can be used reflecting both reliability data from overseas country and workplace failure data that has less samples. As a setting toward difficult situation that securing sufficient failure data cannot be achieved, we composed workplace failure data equivalent to mass observation time 20%(t=17000), 40%(t=24000), 60%(t=31000), 80%(t=38000) and IEEE data by using Bayesian method.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.675-682
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• 2013

Reliability analysis and prediction of next failure time is critical to sustain weapon systems, concerning scheduled maintenance, spare parts replacement and maintenance interventions, etc. Since 1981, many methodology derived from various probabilistic and statistical theories has been suggested to do that activity. Nowadays, many A.I. tools have been used to support these predictions. Support Vector Regression(SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVM and SVR with combining time series to predict the next failure time based on historical failure data. A numerical case using failure data from the military equipment is presented to demonstrate the performance of the proposed approach. Finally, the proposed approach is proved meaningful to predict next failure point and to estimate instantaneous failure rate and MTBF.

• Proceedings of the KIEE Conference
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• summer
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• pp.177-179
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• 2004

In the past decade, the importance and necessity of some studies on reliability evaluation of power system comes from the recent blackout events occurred in the world. Such power system reliability evaluation depends especially on historical outage data. This paper presents reliability model for evaluation in KEPCO systems that is suited to it's propose, and is to show how failure rates and unavailability(Forced Outage Rate) of transmission system components can be determined from the historical outage data of KEPCO systems. The data for these components were made available by KEPCO and KEPRI. A record spanning about 10 years of the historical data was used.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.10
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• pp.480-486
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• 2005

This paper proposes a method to consider an aging failure probability and survival probability of power system components, though only aging failure probability has been considered in existing mean life calculation. The estimates of the mean and its standard deviation is calculated by using Weibull distribution, and each estimated parameters is obtained from Data Analytic Method (Type H Censoring). The parameter estimation using Data Analytic Method is simpler and faster than the traditional calculation method using gradient descent algorithm. This paper shows calculation procedure of the mean life and its standard deviation by the proposed method and illustrates that the estimated results are close enough to real historical data of combustion turbine generating units in Korean systems. Also, this paper shows the calculation procedures of a probabilistic failure prediction through a stochastic data analysis. Consequently, the proposed methods would be likely to permit that the new deregulated environment forces utilities to reduce overall costs while maintaining an are-related reliability index.

• Earthquakes and Structures
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• v.19 no.6
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• pp.471-484
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• 2020

Previous earthquakes show that, structural safety evaluations should include the evaluation of nonstructural components. Failure of nonstructural components can affect the operational capacity of critical facilities, such as hospitals and fire stations, which can cause an increase in number of deaths. Additionally, failure of nonstructural components may result in economic, architectural, and historical losses of community. Accelerations and random vibrations must be under the predefined limitations in structures with high technological equipment, data centers in this case. Failure of server equipment and anchored server racks are investigated in this study. A probabilistic study is completed for a low-rise rigid sample structure. The structure is investigated in two versions, (i) conventional fixed-based structure and (ii) with a base isolation system. Seismic hazard assessment is completed for the selected site. Monte Carlo simulations are generated with selected parameters. Uncertainties in both structural parameters and mechanical properties of isolation system are included in simulations. Anchorage failure and vibration failures are investigated. Different methods to generate fragility curves are used. The site-specific annual hazard curve is used to generate risk curves for two different structures. A risk matrix is proposed for the design of data centers. Results show that base isolation systems reduce the failure probability significantly in higher floors. It was also understood that, base isolation systems are highly sensitive to earthquake characteristics rather than variability in structural and mechanical properties, in terms of accelerations. Another outcome is that code-provided anchorage failure limitations are more vulnerable than the random vibration failure limitations of server equipment.

• 유체기계공업학회:학술대회논문집
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• 2005.12a
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• pp.607-612
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• 2005

Check valves playa vital role in the operation and protection of nuclear power plants. Check valves failure in nuclear power plants often lead to a plant transient or trip. An overview of the failure history of check valves needs to identify key area where resources can be best applied to further improve their reliability, and provide cost effective means for failure reduction. The analysis of historical failure data gives information on the populations of various types of check valves, the systems they are installed in, failure modes, effects, methods of detection, and the mechanisms of the failures. The results presented are based on information derived from operating records, nuclear industry reports, manufacturer supplied information. A majority of check valve failures are caused by improper application. Failure modes are identified for swing and lift check valves. Failures involving improper seating and valve disc stuck comprised the largest percentage of failures.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.279-289
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• 1998

This study deals with the schemes of design, plan and operate maintenance management support systems and with the engineering approach for the solutions to build the maintenance management for the production efficiency. Maintenance Management Information System(MMIS) is the task that must focus on machinery historical data and planned maintenance action. Also the efficient supporting system in a maintenance management is achieved by database which is based on process of machinery's failure history. Designing method of maintenance management information system, maintenance modules are consisted of six factors ; machinery's historical data, lubrication control, check sheet, repair work, availability report, and performance report(control board and detailed reports), and then operators can rapidly utilize data in work place. In the implementation of designed model, program coding has been developed by Visual Basic 3.0. Data insertion, deletion and updating which perform menu screen is implemented by reading data from database. Implementation model based on LAN environment and related data is stored in Microsoft DBMS.