• Geotechnical Engineering
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• v.12 no.6
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• pp.37-50
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• 1996

In this study, we collected data through the investigation of rock slopes of highway. By analyzing the collected data, the main factors of rock slope failure were studied. We studied on the failure types and scales according to rock types and geological structures in many rock slopes of highway. As a result, it was shown that many failed slopes were distributed in the areas of Cretaceous sedimentary rocks of south-eastern part in the Korean Peninsula and the Gneiss Complex in both Kyonggi-Do and Kangwon-Do. According to rock types, the following slope failure types were shown : that igneous rocks had the types of rock fall, plane failure, soil erosion and circular failure but had low failure frequency, and sedimentary rocks had predominantly the type of plane failure. Metamorphic rock showed the types of circular failure, wedge failure and plane failure due to poor rock qualities . According to geological structures, the following slope failure types were shown slope failure in igneous rocks was caused by joints, and in sedimentary rocks by bedding plane, and in metamorphic rocks by faults and poor rock qualities.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1169-1174
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• 2013

Most products are indeed governed by multiple failure modes. However, there are few cases in which reliability analysis applies to only one failure mode at a time. Furthermore, reliability data do not include information about failure modes, or the reliability analysis is performed using a representative failure mode. The Weibull shape parameter for failure modes is more important than one for products in the reliability qualification test. This paper presents reliability analysis methods for a mechanical component with multiple failure modes. These methods include the competing failure modes (CFM) method and the mixed Weibull method. Pneumatic cylinder test data with three failure modes are presented to estimate the shape parameter for each separate failure mode. In addition, reliability measures (B10 life, characteristic life) of the pneumatic cylinder considering three failure modes were compared with those assuming a single failure mode.

• Geomechanics and Engineering
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• v.32 no.1
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• pp.49-68
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• 2023

The three-dimensional failure criterion is essential for maintaining wellbore stability and sand production problem. The convenient factor for a stable wellbore is mud weight and borehole orientation, i.e., mud window design and selection of borehole trajectory. This study proposes a new three-dimensional failure criterion with linear relation of three in-situ principal stresses. The number of failure criteria executed to understand the phenomenon of rock failure under in-situ stresses is the Mohr-Coulomb criterion, Hoek-Brown criterion, Mogi-Coulomb criterion, and many more. A new failure criterion is the extended Mohr-Coulomb failure criterion with the influence of intermediate principal stress (σ₂). The influence of intermediate principal stress is considered as a weighting of (σ₂) on the mean effective stress. The triaxial compression test data for eleven rock types are taken from the literature for calibration of material constant and validation of failure prediction. The predictions on rock samples using new criteria are the best fit with the triaxial compression test data points. Here, Drucker-Prager and the Mogi-Coulomb criterion are also implemented to predict the failure for eleven different rock types. It has been observed that the Drucker-Prager criterion gave over prediction of rock failure. On the contrary, the Mogi-Coulomb criterion gave an equally good prediction of rock failure as our proposed new 3D failure criterion. Based on the yield surface of a new 3D linear criterion it gave the safest prediction for the failure of the rock. A new linear failure criterion is recommended for the unique solution as a linear relation of the principal stresses rather than the dual solution by the Mogi-Coulomb criterion.

• Journal of Drive and Control
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• v.18 no.1
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.569-577
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• 2012

The analysis and management of business failure has been recognized to be important in the area of financial management in the evaluation of firms' performance and the assessment of their viability. To this end, effective failure-prediction models are needed. This paper describes a new approach to prediction of business failure using the total margin algorithm which is a kind of support vector machine. It will be shown that the proposed method can evaluate the risk of failure better than existing methods through some real data.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.85-91
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• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.1
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• pp.39-51
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• 1994

Almost all accelerated life tests assume that no basic failure mechanism changes within the test stresses. But accelerated life test, considering failure mechanism changes, is needed since failure mechanism changes when accelerating beyond the used stress. This paper studies the analysis when the failure mechanism changes within the test stresses. The piecewise linear regression, which the join point of two lines is estimated, is applied In particular, two accelerated life tests, with and without a change in failure mechanism are examined.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.191-196
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• 2007

This paper is reviewed RAM(Reliability, Availability and Maintainability) data table utilized for RAM data management to Urban Maglev Transit. As railway systems become more complex, the RAM requirements are reinforced to ensure that a design meets Reliability, Availability, Maintainability criteria. Therefore, it needs the efficient management for RAM data of railway system to meet RAM target. At this study, RAM data management format is suggested to ensure reliability and maintainability based on acquired experience for overseas rolling stock. This RAM data table and FMECA(Failure Mode Effect Criticality Analysis) table are useful to the calculation of MTBF(Mean Time Between Failure), MTBSF(Mean Time Between Service Failure) and Maintainability. Also, this RAM management table will be efficient to improve the RAM evaluation to Urban Maglev Transit.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.18-24
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• 2020

This research conducted an the failure analysis was performed based on the failure and operation data for Seven years using the Reliability, Availability, Maintainability, and Safety(RAMS) constructed at the operation stage after the opening of the D urban railway. therefore, the risk priority was selected for failure frequency component within the door system that showed high failure. Finally, the goal was to suggest ways to improve the door system. For this purpose, the analysis of thermal characteristics of failed components such as Door Control Unit(DCU) in the door system based on the Seven-year failure analysis data of RAMS was performed. These results were applied to the main component exchange cycle of the door unit, the mean time between failure(MTBF) and mean kilometer between failure(MKBF) values of RAMS increased by 26% in 2017-2018 when the improvement measures were taken, and the MTBF value of DCU was 300,000 hours, which was a 57% improvement in reliability. The results of this thesis identify potential enhancements in reliability and improvements in maintenance of the door system that, if implemented, would contribute to train safety and reduce instances of failure in the future.

• Nuclear Engineering and Technology
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• v.39 no.5
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• pp.591-602
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• 2007

The results of Reactivity-Initiated Accidents (RIA) experiments have been analysed and the main variables affecting the fuel failure propensity identified. Fuel burn-up aggravates the mechanical loading of the cladding, while corrosion, or better the hydrogen absorbed in the cladding as a consequence of corrosion, may under some conditions make the cladding brittle and more susceptible to failure. Experiments point out that corrosion impairs the fuel resistance for RIA transient occurring at cold conditions, whereas there is no evidence of important embrittlement effects at hot conditions, unless the cladding was degraded by oxide spalling. A fuel failure threshold correlation has been derived and compared with experimental data relevant for BWR and PWR fuel. The correlation can be applied to both cold and hot RIA transients, account taken for the lower ductility at cold conditions and for the different initial enthalpy. It can also be used for non-zero power transients, provided that a term accounting for the start-up power is incorporated. The proposed threshold is easy to use and reproduces the results obtained in the CABRI and NSRR tests in a rather satisfactory manner. The behaviour of advanced PWR alloys and of MOX fuel is discussed in light of the correlation predictions. Finally, a probabilistic approach has been developed in order to account for the small scatter of the failure predictions. This approach completes the RIA failure assessment in that after determining a best estimate failure threshold, a failure probability is inferred based on the spreading of data around the calculated best estimate value.