• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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• pp.143-148
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• 2013

In FMEA, occurrence and detectability are not related to only failure modes itself but also their causes. It is assumed that any failure occurs after at least one cause corresponding to failure occurs in advance. Occurrence of the failure mode is described by occurrence time of its cause and elapsed time to the actual failure. Under the periodic monitoring plan, the monitoring interval is another factor to determine the detectability and occurrence of each failure mode. When a failure cause occurs, the failure does not occur if the cause is identified and remedied before it actually occurs. Under this situation, we construct an economic model for prioritizing failure modes. The loss function is based on the unfulfilled mission period. We also provide an optimal monitoring plan with an illustrative example.

• 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.

• Steel and Composite Structures
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• v.16 no.6
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• pp.595-609
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• 2014

The paper is reporting some comparisons between experimental and numerical results in terms of failure mode, failure time and ballistic properties of mild steel sheet. Several projectile shapes have been considered to take into account the stress triaxiality effect on the failure mode during impact, penetration and perforation. The initial and residual velocities as well as the failure time have been measured during the tests to estimate more physical quantities. It has to be noticed that the failure time was defined using a High Speed Camera (HSC). Thanks to it, the impact forces (average and maximum level), were analyzed using numerical simulations together with an analytical description coupled to experimental observations. The key point of the model is the consideration of a shape function to define the pulse loading during perforation.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.467-478
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• 2016

The accelerated failure time model or accelerated life model relates the logarithm of the failure time linearly to the covariates. The parameters in the model provides a direct interpretation. In this paper, we review some newly developed practically useful estimation and inference methods for the model in the analysis of right censored data.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.244-250
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• 1997

The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent Property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability's or components are combined, which results in the dynamic MTTF or system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.11
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• pp.548-556
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• 2005

Reliability evaluation of power distribution system is very important to both power utilities and customers. It present the probabilistic number and duration of interruption such as failure rate, SATDI, SAIFI, and CAIDI. However, it has a fatal weakness at reliability index because of accuracy of failure rate. In this paper, the Time-varying Failure Rate(TFR) of power distribution system equipment is extracted from the recorded failure data of KEPCO(Korea Electric Power Corporation) in Korea. For TFR extraction, it is used that the fault data accumulated by KEPCO during 10 years. The TFR is approximated to bathtub curve using the exponential(random failure) and Weibull(aging failure) distribution function. In addition, Kaplan-Meier estimation is applied to TFR extraction because of incomplete failure data of KEPCO. Finally, Probability plot and regression analysis is applied. It is presented that the extracted TFR is more effective and useful than Mean Failure Rate(MfR) through the comparison between TFR and MFR

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.34
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• pp.139-146
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• 1995

This study is concerned with cost analysis in periodic maintenance policy. Generally periodic maintenance policy in which item is repaired periodic interval times. And in the article minimal repair is considered. Minimal repair means that if a unit fails, unit is instantaneously restored to same hazard rate curve as before failure. In the paper periodic maintenance policy with minimal repair is as follows; Operating unit is periodically replaced in periodic maintenance time, if a failure occurs between minimal repair and periodic maintenance time, unit is replaced by a spate until the periodic time comes. Also unit undergoes minimal repair at failures in minimal-repair-for-failure interval. Then total expected cost per unit time is calculated according to maintenance period and scale parameter of failure distribution. Total cost factors ate included operating, fixed, minimal repair, periodic maintenance and replacement cost Numerical example is shown in which failure time of system has erlang distribution.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.287-295
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• 1995

This paper is concerned with cost analysis model in periodic maintenance policy. Generally periodic maintenance policy in which item is repaired periodic interval times. And in the article minimal repair is considered. Mimimal repair means that if a unit fails, unit is instantaneously restored to same hazard rate curve as before failure. In the paper periodic maintenance policy with minimal repair is as follows; Operating unit is periodically replaced in periodic maintenance time, if a failure occurs between minimal repair and periodic maintenance time, unit is replaced by a new item until tile periodic maintenance time comes. Also unit undergoes minimal repair at failures in minimal-repair-for-failure interval. Then total expected cost per unit time is calculated according to scale parameter of failure distribution. Maintenance cost factors are included operating, fixed, minimal repair, periodic maintenance and new item replacement cost. Numerical example is shown in which failure time of system has weibull distribution.

• Tunnel and Underground Space
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• v.19 no.6
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• pp.558-566
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• 2009

Time-dependent behavior is a basic mechanical property of rocks. Predicting the failure time of rock structures by analyzing the time-dependent characteristic is important and problematic. It is tried to predict the failure time of tunnel, slope & laboratory creep test specimen from measured displacement(or strain) and rate with relationship suggested by Voight($\ddot{\Omega}=A\dot{\Omega}^\alpha$, where $\Omega$ is a measurable quantity such as strain & displacement and A & $\alpha$ are constants). A & $\alpha$ are estimated through applying the nonlinear least square method to the single and double integrated Voight's equations and utilized to predict the failure time. Predicted failure time is in accordance with real one except minor error. Linear inverse rate method applied to creep strain and rate yields a poor linear correlation of data and precision of predicted failure time is not better than methods using strain and rate.

• Journal of Applied Reliability
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• v.10 no.1
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• pp.1-9
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• 2010

In general, the performance of a component degrades as time goes by and failure of a component occurs when the performance degradation reaches a pre-specified level. It is difficult to obtain the failure time distribution data or the necessary number of failure data especially for the metal or machine part. Thus, a design of reliability qualification test based on performance distribution is more effective than failure time distribution. In this study, a performance-based reliability qualification test is developed and a numerical example is provided to illustrate the use of the developed reliability qualification test. This approach could be applied to many kinds of metal or machine part whose magnitude of strength could not be evaluated during at any random points but judgement can be made by only failure of the part. Besides, it is also possible that any parts which have a similar failure characteristics could be applicable to the developed reliability qualification test.