• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 1997.04a
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• pp.269-279
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• 1997

An investigation was peformed to study the predicting the joint strength of mechanical fasteners. Bearing failure is most important failure mode for designing joint. So in this study, the prediction method in consideration with bearing failure was chosen. In the proposed method, the characteristic length is combined with the Yamada-Sun failure criterion, Tsai-Hill failure criterion and characteristic length for Tension and Compression is determined from investigation. Especially the length of compression is determined from the "bearing failure test" that newly conceived to take bearing failure into consideration. The proposed prediction method was applied to quasi-isotropic carbon/epoxy joint showing net-tension and bearing failure experimentally. Good agreement was found between the predicted and experimental result for each joint geometry.

• Journal of the Korean Society for Railway
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• v.9 no.4 s.35
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• pp.419-424
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• 2006

In this paper we study on a method to predict and to demonstrate the reliability of the Korea high speed train control system in quantitative point of view. For the prediction of the reliability in train control system which is composed of electronic parts, Relax Software 7.7 automation tool is employed and MIL-HDBK-217 Handbook that is a standard for the prediction of the failure rate in electronic components is used. Mean Time Between Failure (MTBF) is predicted based on the failure rate of the subsystems, State Modeling and Markov Modeling method is used to express a reliability function of the train control system composed by hardware redundancy as a function of time. We propose a Reliability Test which is performed on the level of the subsystems and Failure Report, Analysing, Correction action system which use the test operation data to prove the predicted reliability.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.6
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• pp.804-811
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• 2010

The fatigue crack propagation is stochastic in nature, because the variables affecting the fatigue behavior are random and have uncertainty. Therefore, the fatigue life prediction is critical for the design and the maintenance of many structural components. In this study, fatigue experiments are conducted on the specimens of magnesium alloy AZ31 under various conditions such as thickness of specimen, the load ratio and the loading condition. The probability distribution fit to the fatigue failure life are investigated through a probability plot paper by these conditions. The probabilities of failure at various conditions are also estimated. The fatigue design life is predicted by using the Weibull distribution.

• Journal of the Microelectronics and Packaging Society
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• v.20 no.3
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• pp.7-10
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• 2013

This study presents an approach to predict insulation resistance failure of multilayer ceramic capacitors (MLCCs) using non-linear modeling. A capacitance aging model created by non-linear modeling allowed for the prediction of insulation resistance failure. The MLCC data tested under temperature-humidity-bias testing conditions showed that a change in capacitance, when measured against a capacitance aging model, was able to provide a prediction of insulation resistance failure.

• Journal of the Korean Society of Safety
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• v.23 no.6
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• pp.7-13
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• 2008

It is important to prevent roll failure in hot rolling process for reducing maintenance cost and production loss. The relationship between rolling pass schedule and the work roll wear profile will be presented. The roll wear pattern is related with roll catastrophic failure. The irregular and deep roll wear pattern should be removed by On-line Roll Grinder(ORG) for roll failure prevention. In this study, a computer roll wear prediction model under real process working condition is developed and evaluated with hot rolling pass schedule. The method of building wear calculation functions for center portion abrasion and marginal abrasion respectively was used to develop a work roll wear prediction mathematical model. The three type rolling schedule are evaluated by wear prediction model. The optimum roll grinding methods is suggested for schedule tree rolling technique.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.485-486
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• 2006

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.8 s.239
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• pp.1070-1077
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• 2005

The $40\%$ of wall thickness criterion which has been used as a plugging rule is applicable only to a single cracked steam generator tubes. In the previous studies performed by authors, several failure prediction models were introduced to estimate the plastic collapse pressures of steam generator tubes containing collinear or parallel two adjacent axial through-wall cracks. The objective of this study is to examine the failure prediction models and propose optimum ones for non-aligned two axial through-wall cracks in steam generator tubes. In order to determine the optimum ones, a series of plastic collapse tests and finite element analyses were carried out for steam generator tubes with two machined non-aligned axial through-wall cracks. Thereby, either the plastic zone contact model or COD based model was selected as the optimum one according to axial distance between two clacks. Finally, the optimum failure prediction model was used to demonstrate the conservatism of flaw characterization rules for various multiple flaws according to ASME code.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.123-131
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• 2020

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.2
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.3
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• pp.95-107
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• 2020

The failures of the agricultural reservoirs that most have more than 50 years, have increased due to the abnormal weather and localized heavy rains. There are many studies on the prediction of damage from reservoir collapse, however, these referenced studies focused on evaluating reservoir collapse as single unit and applyed to one and two dimensional hydrodynamic model to identify the fluid flow. This study is to estimate failure probability of spillway, sliding, bearing capacity and overflowing targeting small and medium scale agricultural reservoirs. In addition, we calculate failure probability by complex mode. Moreover, we predict downstream flood damage by reservoir failure applying three dimensional hydrodynamic model. When the reservoir destroyed, the results are as follows; (1) the flow of fluid proceeds to same stream direction and to a lower slope by potential and kinetic energy; (2) The predicted damage in downstream is evaluated that damage due to building destruction is the highest.