• Journal of Power Electronics
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• v.18 no.5
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• pp.1608-1617
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• 2018

The rapid and correct isolation of faulty submodules (SMs) is of great importance for improving the reliability of modular multilevel converters (MMCs). Therefore, a fast diagnosis method containing fault detection and fault location determination was presented in this paper. An improved incremental predictive model of arm current was proposed to detect failures, and the multi-step prediction method was used to eliminate the negative impact of disturbances. Moreover, a control method was proposed to strengthen the fault characteristics to rapidly locate faulty arms and faulty SMs by detecting the variation rate of the SM capacitor voltage. The proposed method can rapidly and easily locate faulty SMs under different load conditions without the need for additional sensors. The experimental results have validated the effectiveness of the proposed method by using a single-phase MMC with four SMs per arm.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.6
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• pp.391-405
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• 2015

A global tsunami prediction system was suggested for a distant tsunami using a finite fault model and a cyclic boundary condition. The possibility of the suggested system as a distant tsunami response system was checked by applying it into the case of 2014 Chile tsunami. A comparison between the numerical results(tsunami height and arrival time) with different conditions (boundary condition, governing equation, grid size and fault model) and measured data (DART buoy, tide station) showed the importance of the finite fault model and the cyclic boundary condition.

• 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.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.33-40
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• 2003

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development costs because the problems in early phases largely affect the quality of the late products. Real-time systems such as telecommunication systems are so large that criticality prediction is mere important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing causes of the prediction results and low extendability. This paper builds a new prediction model, GAM, based on Genetic Algorithm. GAM is different from other models because it produces a criticality function. So GAM can be used for comparison between entities by criticality. GAM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering Internal characteristics and accuracy of prediction.

• The Journal of Engineering Geology
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• v.26 no.2
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• pp.187-196
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• 2016

We conducted three-dimensional finite element analysis to predict the presence of upcoming fault zones during tunneling. The analysis considered longitudinal displacements measured at tunnel face, and used 28 numerical models with various fault attitudes. The x-MR (moving range) control chart was used to analyze quantitatively the effects of faults distributed ahead of the tunnel face, given the occurrence of a longitudinal displacement. The numerical models with fault were classified as fault gouge, fault breccia, and fault damage zones. The width of fault cores was set to 1 m (fault gouge 0.5 m and fault breccia 0.5 m) and the width of fault damage zones was set to 2 m. The results, suggest that fault centers could be predicted at 2~26 m ahead of the tunnel face and that faults could be predicted earliest in the 45° dip model. In addition, faults could be predicted earliest when the angle between the direction of tunnel advance and the strike of the fault was smallest.

• KNOM Review
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• v.24 no.1
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• pp.13-19
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• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1213-1216
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• 1998

This paper presents the result of a feasibility study for the prediction method of fault symptoms on 22.9kV distribution line. In this paper, real distribution data was collected and analyzed to isolate failure signatures or parameters which were distinct behaviors before and after failure incident. A new strategy of analysis-based (event-date concept) prediction algorithm for the distribution insulators and a developed model system were also discussed.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1401-1408
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• 2000

Critical prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development cost because the problems in early phases largely affected the quality of the late products. Real-time systems such as telecommunication system are so large that criticality prediction is more important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing cause of the prediction results and low extendability. In this paper, we propose a criticality prediction model using fuzzy rulebase constructed by genetic algorithm. This model makes it easy to analyze the cause of the result and also provides high extendability, high applicability, and no limit on the number of rules to be found.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.135-137
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• 2006

To improve device yield and throughput, faults in plasma processing equipment should be quickly and accurately diagnosed. Despite many useful information of ex-situ sensor measurements, their applications to recognize plasma faultshave not been investigated. In this study, a new technique to identify fault causes by recognizing X-ray photoelectron spectroscopy (XPS) using neural network and continuous wavelet transformation (CWT). The presented technique was evaluated with the plasma etch data. A totalof 17 experiments were conducted for model construction. Model performance was investigated from the perspectives of training error, testing error, and recognition accuracy with respect to various thresholds. CWT-based BPNN models demonstrated a higher prediction accuracy of about 26%. Their advantages over pure XPS-based models were conspicuous in all three measures at small networks.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.90-93
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• 2014

In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm's performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.