• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.859-870
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• 2011

The purpose of this paper is to develop reliability analysis procedures for repairable systems with interval failure time data and apply the procedures for assessing the storage reliability of a subsystem of a certain type of guided missile. In the procedures, the interval failure time data are converted to pseudo failure times using the uniform random generation method, mid-point method or equispaced intervals method. Then, such analytic trend tests as Laplace, Lewis-Robinson, Pair-wise Comparison Nonparametric tests are used to determine whether the failure process follows a renewal or non-renewal process. Monte Carlo simulation experiments are conducted to compare the three conversion methods in terms of the statistical performance for each trend test when the underlying process is homogeneous Poisson, renewal, or non-homogeneous Poisson. The simulation results show that the uniform random generation method is best among the three. These results are applied to actual field data collected for a subsystem of a certain type of guided missile to identify its failure process and to estimate its mean time to failure and annual mean repair cost.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.4
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• pp.117-124
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• 2015

RAM (Reliability, Availability and Maintenance) has been applied to design and analysis tools which affects system's operational sustainability and its life cycle cost as RAM-c(2009 DoD). RAM-c plays also an important role to guarantee the system engineering for mission assurance. Reliability is highly related to the probability of system failure. Availability means mission capability or the condition of ready to mission. Maintenance includes both the repair to recover the system in the event of failure/unexpected breakdown and proactive maintenance to prolong the design service life of the system or machinery. It is the purpose that this paper is to analyze and conclude the objective service life of UAV. The more UAV is operated, the less the level of its reliability becomes. Repairing failures and supplying spare parts on time, system reliability could be improved up until the time over target. Applying statistical Weibull distributions, this paper suggests the analysis of the design service life and economic life of UAV based on RAM-c with operational data.

• Journal of Korean Society of Water and Wastewater
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• v.11 no.1
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• pp.21-32
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• 1997

Optimum design of water distribution network(WDN) in many times means just reducing redundancy. Given only a few situations are taken into consideration for such design, WDN deprived of inherited redundancy may not work properly in some unconsidered cases. Quantifying redundancy and incorporating it into the optimal design process will be a way of overcoming just reduction of redundancy. Expected shortage is developed as a reliability surrogate in WDN. It is an indicator of the frequency, duration and severity of failure. Using this surrogate, Expected Shortage Optimization Model (ESOM) is developed. ESOM is tested with an example network and results are analyzed and compared with those from other reliability models. The analysis results indicate that expected shortage is a quantitative surrogate measure, especially, good in comparing different designs and obtaining tradeoff between cost and. reliability. In addition, compared other models, ESOM is also proved useful in optimizing WDN with reliability and powerful in controlling reliability directly in the optimization process, even if computational burden is high. Future studies are suggested which focus on how to increase applicability and flexibility of ESOM.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.467-471
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• 2014

The natural frequencies of a mechanical system are determined by the system parameters such as masses and stiffness of the system. Since material irregularities and manufacturing tolerances always exist in most of practical engineering situations, the system parameters always have uncertainties. As the uncertainties of the parameters increase, the uncertainties of the system natural frequencies also increases. Then, the reliability of the system deteriorates. So, the uncertainty of the system natural frequencies should be analyzed accurately and considered in the design of the system. In order to analyze the uncertainty of the system natural frequencies employing most of existing uncertainty analysis methods, the probability distributions of the uncertain system parameters should be identified. In most practical situations, however, identification of the probability distributions is almost impossible because of limited time and cost. For that case, the reliability should be estimated based on finite samples of the system parameters. In this paper, sample based reliability estimation method employing extreme value theory was proposed. Using the proposed estimation method, sample based reliability design of the system natural frequencies was conducted.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.529-534
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• 2003

Within industry induction motors have a broad application area to drive pumps, fans, elevators and electric trains. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.645-648
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• 2003

Induction motors are a critical component of industrial processes. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyses the motor's supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Journal of the Korean GEO-environmental Society
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• v.18 no.9
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• pp.11-18
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• 2017

Numerical analysis method for prediction of pile behaviour characteristics has widely been used in detail design process before construction because field static loading test requires high cost. However, the reliability verification of numerical analysis of result is not permitted compare with field test. In this study, to verify the numerical analysis results, pile behaviour prediction was compared with field static loading test results. For exact analysis of interaction between pile and ground, soil investigation and in-situ test such as boring, SPT and bore-hole shear test were performed before pile static loading test. During the static loading test, pile behaviour characteristics were analyzed under every loading condition. After static pile loading test, numerical analysis was carried out under same condition with static pile loading test. In the numerical analysis, to apply same loading condition with each loading condition in the field test and to compare with between the results of numerical analysis, the field test results for reliability were verified with the results of numerical analysis.

• Convergence Security Journal
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• v.17 no.3
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• pp.95-101
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• 2017

Reliability is the priority matter in guided weapon systems. The reliability prediction data is used during the devel opment stage as the manufacturing cost is very high and the production quantity if quite limited. At the same time it takes relatively a long period of time to acquire a reliable operation data set after deployment such that in order t o determine the operational reliability, weapons must be tested and analyzed in real operating environments. For the research, the life distributions were estimated by using actual operation data and the reliability was calculated by ap plying the method of least squares and maximum likelihood estimation. Also, the comparisons were made between pr edicted reliability and actual operational reliability. As a result, the actual reliability of each system was higher than predicted reliability and it was considered that such a difference was caused by the fact that the application of the l atest designing technology and improved parts to the guided weapon systems was not reflected on the estimation of predicted reliability. It was possible to confirm the actual operational reliability of domestic (ROK) guided weapon sy stems through this research and the methods used here will contribute to the reliability analyses for the future guide d weapon systems to be developed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.21
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• pp.43-50
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• 1990

Fault tree analysis(FTA) is available to the engineer for determining reliability of complex industrial safety system. Therefore quantitative aspects of FTA greatly multiply its power this paper proceeds of presenting the methodology of FTA, including an approach to constructing in fault tree. A working guide to the use of FTA for the purpose of cost/benefit determination in industrial safety system is given. Finally, an analytic method for uncertainty analysis of the top event of a complex system is described.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.544-544
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• 2014

Energy harvesting technology, which scavenges electric power from ambient, otherwise wasted, energy sources, has been explored to develop self-powered wireless sensors and possibly eliminate the battery replacement cost for wireless sensors. Among ambient energy sources, vibration energy can be converted into electric power through a piezoelectric energy harvester. For the last decade, although tremendous advances have been made in design methodology to maximize harvestable electric power under a given vibration condition, the research in reliability assessment to ensure durability has been stagnant due to the complicated nature of the multiple failure modes of a piezoelectric energy harvester, such as the interfacial delamination, fatigue failure, and dynamic fracture. Therefore, this study presents the first-ever system reliability analysis for multiple failure modes of a piezoelectric energy harvester using the Generalized Complementary Intersection Method (GCIM), while accounts for the energy conversion performance. The GCIM enables to decompose the probabilities of high-order joint failure events into probabilities of complementary intersection events. The electromechanically-coupled analytical model is implemented based on the Kirchhoff plate theory to analyze its output performances of a piezoelectric energy harvester. Since a durable as well as efficient design of a piezoelectric energy harvester is significantly important in sustainably utilizing self-powered electronics, we believe that technical development on system reliability analysis will have an immediate and major impact on piezoelectric energy harvesting technology.