• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• Journal of the Korean GEO-environmental Society
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• v.10 no.6
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• pp.35-48
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• 2009

An excessive ground displacement occurs with excavating tunnel in a fault zone because the fault has properties of soft ground in generally. It may have had a bad influence to adjacent structure. So, rapid reduction of ground strength by groundwater inflow should be prevented. It must be established for an impervious and reinforcing effect of ground to ensure a tunnel stability. The ground settlement and reinforcing effects were estimated by numerical analyses on tunnel through 570 m sector in Yangsan fault zone of Keongbu high-speed railway. Settlements evaluated by numerical analysis is similar to those calculated by using equation of Loganathan & Poulo. It was shown that reliable estimate of ground settlement by applying a prediction equation is possible. Applicability of adopted tunnel reinforcement method in fault zone was investigated by results of pilot construction and numerical analysis. Results from this study indicate that the adopted reinforcement method make tunnel displacements and member stresses restrain in design criteria.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.995-1000
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• 2017

In this paper, we present an electrical arc detection algorithm using the mash-up analysis technique which is the core technology for the autonomous electrical safety management system(AESMS) of the multi-unit dwellings. The mash-up analysis technique analyzes the voltage, load current, zero phase current data simultaneously to judge arc faults. In order to develop the arc fault detection algorithm, the characteristics of series arc and parallel arc were analyzed. Also, we propose the mash-up analysis technique that analyzes waveforms of voltage, load current, and zero phase current at the same time. The arc fault detection algorithm was developed using the mash-up analysis technique. The developed algorithm can prevent electrical disasters in an effective way through accident prediction, and it will be used as a basic technology to introduce an autonomous electrical safety management system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.230-231
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• 2019

This study is a development on condition based maintenance(CBM) technology which is a core item of future autonomous ships. It is developing to design & installation of condition monitoring system and acquisition & processing of data from ongoing ships for fault prediction & prognosis of engine in operation. The ultimate goal of this study is to develop a predicts and decision support software for marine engine faults. To do this, the FMEA and fault tree analysis of the main engine should be accompanied by the analysis of classification of system, identification of the components, the type of faults, and the cause and phenomenon of the failure. Finally, the CBM system solution software could predict and diagnose the failure of main engine through integrated analysis for bid-data of ongoing ships and engineering knowledge. Through this study, it is possible to pro-actively cope with abnormal signals of engine and to manage efficiently, and as a result, expected that marine accident and ship operation loss during navigation will be prevented in advance.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1998-2008
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• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.107-113
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• 2020

A number of studies have been conducted on predicting software faults, but most of them have been supervised models using labeled data as training data. Very few studies have been conducted on unsupervised models using only unlabeled data or semi-supervised models using enough unlabeled data and few labeled data. In this paper, we produced new semi-supervised models using tree algorithms in the self-training technique. As a result of the model performance evaluation experiment, the newly created tree models performed better than the existing models, and CollectiveWoods, in particular, outperformed other models. In addition, it showed very stable performance even in the case with very few labeled data.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.87-91
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• 2019

The diagnosis of the failure for the existing electrical facilities was based on regular preventive maintenance, but this preventive maintenance was limited in preventing a lot of cost loss and sudden system failure. To overcome these shortcomings, fault prediction and diagnostic techniques are critical to increasing system reliability by monitoring electrical installations in real time and detecting abnormal conditions in the facility early. As the performance and quality deterioration problem occurs frequently due to the increase in the number of users of the motor pump, the purpose is to build an intelligent control system that can control the motor pump to maximize the performance and to improve the quality and reliability. To this end, a vibration sensor, temperature sensor, pressure sensor, and low water level sensor are used to detect vibrations, temperatures, pressures, and low water levels that can occur in the motor pump, and to build a system that can identify and diagnose information to users in real time.

• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.595-601
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• 2017

Due to the electrification of railways, fault at the traction line is increasing year by year. So importance of the fault locator is growing higher. Nevertheless at the field traction line, it is difficult to locate accurate fault point due to various conditions. In this paper railway feeding system current loop equation was simplified and generalized though measured data. And substation, train power data were measured under synchronized condition. Finally catenary impedance was predicted through generalized equation. Also simulation model was designed to figure out the effect of load current for train at same location. Train current was changed from min to max range and catenary impedance was compared at same location. Finally, power measurement was performed in the field at train and substation simultaneously and catenary system impedance was predicted and calculated. Through this method catenary impedance can be measured more easily and continuously compared to the past method.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.329-336
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• 2008

The facilities with new technologies in the recent power plants become larger and need a lot of high cost for maintenance due to stop operations and accidents from the operating machines. Therefore, it claims new systems to monitor the operating status and predict the faults of the machines. This research classifies the normal/abnormal status of the machines into 5 levels which are normal-level/abnormal-level/care-level/dangerous-level/fault and develops the new system that predicts faults without stop operation in power plants. We propose the regional segmentation technique in the frequency domain from the data of the operating machines and generate the Peak-codes similar to the Bar-codes, The high efficient and economic operations of the power plants will be achieved by carrying out the pre-maintenance at the care level of 5 levels in the plants. In order to be utilized easily at power plants, we developed the algorithm appling to a notebook computer from signal acquisition to the discrimination.