• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.134-137
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• 2020

Recently, there are emphasized to support the maintenance and management system of vessels using acquired data from engine part equipment. But, there are limitations for data exchange and management. To solve the problem, the ISO published ISO 19847 and 19848. In this paper, we analyze the ISO 19848 requirements related to identify data channel ID for ship equipment, and propose the examples for applying encoding techniques. In addition, we suggest the proposed technique for applying of managing the failure and maintenance type of the ship's engine part facilities by examples. If this method is applied, the vessel's equipment can exchange data through the sharing of the code table, and express what response is needed or acted, including where the failure occurred.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.6
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• pp.781-787
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• 2010

Forecasting possible failure characteristics is very important in maintenance planning because it helps in predicting any future failures and determining the optimum replacement interval. This paper examines the time.to-failure distribution of the transfer gearbox of a J79 engine by using a probability plotting technique which is one of the most convenient techniques for reliability analysis. Various probability distributions are evaluated for determining the suitable probability distribution of the failure data of the transfer gearbox, and the resulting correlation coefficient indicates that failure data have a lognormal distribution. The expected number of unscheduled maintenance actions and the optimum replacement interval for various values of cost ratios are determined.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.366-372
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• 2020

The Army's rocket missile is a one-shot weapon system, which is produced and used for only one mission, and requires high reliability. While reliability analysis with failure data can result in underestimation of the life distribution, reliability analysis with all the non-failure data can result in overestimation of the life distribution. Under or overestimation of the life distribution can lead to cost increase by early disposal or complete observation of all rocket missiles. In order to overcome this problem, the Army suggests the guideline of the number of samples from non-failure data for reliability analysis with failure data. However, the currently used sampling method can generate errors for predicting the failure rate. To solve this problem, this study proposes a new sampling procedure for predicting a future failure rate using non-failure data. The comparison test between the currently used sampling method and the proposed sampling method is conducted and the result shows that the proposed sampling method can predict the future failure rate more accurately.