• 한국신뢰성학회지:신뢰성응용연구
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• 제17권2호
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• pp.92-102
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• 2017

Purpose: Reliability prediction standards consider environmental conditions, such as temperature, humidity and vibration in order to predict the reliability of the electronics components. There are many types of standards, and each standard has a different failure rate prediction model, and requires different environmental conditions. The purpose of this study is to make a sensitivity analysis by changing the temperature which is one of the environmental conditions. By observing the relation between the temperature and the failure rate, we perform the sensitivity analysis for standards including MIL-HDBK-217F, RiAC-HDBK-217Plus and FIDES. Methods: we establish environmental conditions in accordance with maneuver weapon systems's OMS/MP and mission scenarios then predict the reliability using MIL-HDBK-217F, RiAC-HDBK-217Plus and FIDES through the case of DC-DC Converter. Conclusion: Reliability prediction standards show different sensitivities of their failure rates with respect to the changing temperatures.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 1997년도 제8회 학술강연회논문집
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• pp.269-279
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• 1997

본 연구에서는 탄소/에폭시 복합재료의 기계적 결합부의 결합강도 예측을 위한 구조해석과 실험을 수행했다. 복합재료 구조물의 결합부 설계에 있어 베어링파괴는 대단히 중요한 파괴형태 중 하나이다. 그래서 베어링 파괴를 해석적으로 예측하고 실험적으로 확인하였다. 순수인장 파괴(Net Tension Failure)와 베어링 파괴(Bearing Failure) 실험을 위해서 각각 두 가지 형상의 시편을 선택했다. 기계적 결합강도 예측에 사용된 방법은 특성길이(Characteristic Length)법과 연관된 Yamada-Sun 파괴기준(Failure Criterion)과 Tsai-Hill 최대일 이론이다. 그리고 인장특성길이와 압축특성길이는 실험을 통하여 얻어지며, 특히 압축특성길이 결정은 최근에 착안된 베어링파괴 실험으로부터 결정됐다. 위와 같은 예측 방법을 준등방성(Quasi - Isotropic) Carbon Epoxy HT245/RS3232에 적용하였다. 연구결과, 이론적인 복합재료 파괴예측이 실험결과와 잘 일치함을 확인할 수 있다.

• 한국철도학회논문집
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• 제9권4호
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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.

• 한국생산제조학회지
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• 제19권6호
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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.

• 마이크로전자및패키징학회지
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• 제20권3호
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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.

• 한국안전학회지
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• 제23권6호
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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.

• 대한기계학회논문집A
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• 제29권8호
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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.

• 산업경영시스템학회지
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• 제43권1호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 추계학술대회 논문집
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• pp.485-486
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• 2006

• 한국지진공학회논문집
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• 제28권2호
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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.