• 한국신뢰성학회지:신뢰성응용연구
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• 제16권3호
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• pp.171-179
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• 2016

Purpose: The purpose of this study is to provide a comparison of reliability prediction models between MIL-HDBK-217F and Telcordia SR-332 and to analyze the reliability prediction results. Methods: The reliability of industrial tablet PC was predicted using MIL-HDBK-217F and Telcordia SR-332. To analyze the results, sensitivity analysis was conducted. Results: The reliability of MIL-HDBK-217F was predicted lower than the one of Telcordia SR-332. Considering the factors such as temperature, quality and environment, those provided reliability change of a particular part which highly contribute to the system failure. Conclusion: It is necessary to design the industrial tablet PC that consists of integrated circuits such as microprocessor and memory considering the operating temperature and quality factors.

• 한국신뢰성학회지:신뢰성응용연구
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• 제17권2호
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• pp.103-111
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• 2017

Purpose: Accelerated degradation tests can speed time to market and reduce the test time and costs associated with long term reliability tests to verify the required service life of a product or material. This paper proposes a service life prediction method for components or materials using an accelerated degradation tests based on the relationships between temperature and the rate of failure-causing chemical reaction. Methods: The relationship between performance degradation and the rate of a failure-causing chemical reaction is assumed and least square estimation is used to estimate model parameters from the degradation model. Results: Methods of obtaining acceleration factors and predicting service life using the degradation model are presented and a numerical example is provided. Conclusion: Service life prediction of a component or material is possible at an early stage of the degradation test by using the proposed method.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 봄 학술발표회 논문집
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• pp.897-902
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• 2002

The failure of flat plate connection is successive failure process accompanying with stress redistribution, hence it is necessary to compute the contributions of each resistance components at ultimate state. In the present study, the interactions of resultant forces at each faces of connection, i.e. shear, bending moment and torsional moment are considered in the assessment of strength of slab. As a result the strength prediction model for connection is made up as combination of bending resistance, shear resistance and torsional resistance. The proposed method is verified by the experimental data and numerical data of continuous slabs.

• 산업경영시스템학회지
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• 제22권52호
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• pp.171-180
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• 1999

Industrial equipment reliability improvement and maintenance is gaining attention as the next great opportunity for manufacturing productivity improvement. Reactive maintenance is expensive because of extensive unplanned downtime and damage to machinery. To avoid such an unplanned machine downtime, it is needed to use proactive maintenance approach by either using historical maintenance data or by sensing machine conditions. This paper discusses failure diagonosis and prediction based on the condition-based maintenance and reliability technique. Thus, by enabling such a framework, it can bring us more efficient planning and execution of maintenance to reduce costs and/or increase profits.

• 한국신뢰성학회지:신뢰성응용연구
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• 제5권4호
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• pp.451-463
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• 2005

This paper describes procedure to build a generic reliability analysis database structure and failure data during the conceptual design phase. Reliability prediction utilizing such supporting database system enable the designer to quantify the effects of varying design and operational constrains on mechanical component's failure rate. This leads to compare competing alternatives to the reliability improvement efforts.

• 한국정밀공학회지
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• 제24권3호
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• pp.84-90
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• 2007

As a result of vehicle maintenance of rocker arm shaft for 4-cylinder SOHC engine, failure stress analysis of rocker arm shaft is needed. Because more than 30% of vehicles investigated have been fractured. Failure stress analysis is classified into an naked eyes, microscope, striation and X-ray fractography etc. Failure stress analysis by using striation is already established technology as means for seeking cause of fracture. But, although it is well known that striation spacing corresponds to the crack growth rate da/dN, it is not possible to determine ${\sigma}_{max}\;and\;{\sigma}_{min}$ under service loading only from striation spacing. This is because the value of striation spacing is influenced not only by ${\Delta}K$ but also by the stress ratio R. In the present paper, we determine the stress ratio using orthogonal array and ANOVA, and propose a prediction method of failure stress which is combined with FEM and striation.

• International Journal of Aeronautical and Space Sciences
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• 제17권4호
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• pp.565-578
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• 2016

A probabilistic based systems approach is addressed in this study for the reliability prediction of solid rocket motors (SRM). To achieve this goal, quantitative Failure Modes, Effects and Criticality Analysis (FMECA) approach is employed to determine the reliability of components, which are integrated into the Fault Tree Analysis (FTA) to obtain the system reliability. The quantitative FMECA is implemented by burden and capability approach when they are available. Otherwise, the semi-quantitative FMECA is taken using the failure rate handbook. Among the many failure modes in the SRM, four most important problems are chosen to illustrate the burden and capability approach, which are the rupture, fracture of the case, and leak due to the jointed bolt and O-ring seal failure. Four algorithms are employed to determine the failure probability of these problems, and compared with those by the Monte Carlo Simulation as well as the commercial code NESSUS for verification. Overall, the study offers a comprehensive treatment of the reliability practice for the SRM development, and may be useful across the wide range of propulsion systems in the aerospace community.

• 한국신뢰성학회지:신뢰성응용연구
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• 제16권2호
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• pp.98-103
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• 2016

Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client's manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client's manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2002년도 가을 학술발표회 논문집
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• pp.513-520
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• 2002

The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.

• Computers and Concrete
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• 제28권5호
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• pp.507-519
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• 2021

In this study, a new procedure was proposed in order to predict the crack pattern and failure mode of steel fiber reinforced concrete (SFRC) corbels. Moreover, an experimental study was carried out in order to investigate the effect of several parameters, such as compressive strength, tensile strength, steel fiber ratio, shear span on the mechanical behavior of SFRC corbels in detail. Totally, 24 RC and SFRC corbels were prepared for the experimental study. Experimental results indicate that each investigated parameter has noticeable effect on the load capacity and failure mode of SFRC corbels. Moreover, finite element (FE) model of the tested corbels were prepared and efficiency of FE model was investigated for further studies. Comparison of FE and experimental results show that there is an acceptable fit between them regarding load capacity and crack patterns. Thereafter, parametric study was carried out via FE analyses in order to obtain a methodology for crack pattern and failure mode prediction of SFRC corbels. As a result of parametric studies, a new procedure was proposed as flowcharts in order to predict the failure mode of SFRC corbels for normal and high strength concrete class separately.