• 산업공학
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• 제25권2호
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• pp.170-177
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• 2012

Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2009년도 추계 학술논문 발표대회
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• pp.183-189
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• 2009

In this study, the growth diagnosis of root is analyzed through plant's auxanology point of view, and the inductive root barrier ability of panel type waterproofing method which is designed to deal with it, is confirmed positively through long term(2 years) mock-up test. Moreover, basic ideas for inductive root barrier design in joint is presented through this study. The experiment result for the root barrier of sealed A-type during 24 months, there were no damages found on the waterproofing layer. -urethane sealing material was used to apply for waterproofing of joint- for roots. As the result, it was confirmed that it is possible to maintain the root barrier of method through applying inductive root barrier design such as the installation of decreasing space of bearing power which considers the growth diagnosis of root, even if the root barrier was not secured.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.295-300
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• 1998

In this paper, a real time diagnostic algorithm fur estimating the impact location by loose parts is proposed. It is composed of two modules such as the alarm discrimination module (ADM) and the impact-location estimation module(IEM). ADM decides whether the detected signal that triggers the alarm is the impact signal by loose parts or the noise signal. When the decision from ADM is concluded as the impact signal, the beginning time of burst-type signal, which the impact signal has usually such a form in time domain, provides the necessary data fur IEM. IEM by use of the arrival time method estimates the impact location of loose parts. The overall results of the estimated impact location are displayed on a computer monitor by the graphical mode and numerical data composed of the impact point, and thereby a plant operator can recognize easily the status of the impact event. This algorithm can perform the diagnosis process automatically and hence the operator's burden and the possible operator's error due to lack of expert knowledge of impact signals can be reduced remarkably. In order to validate the application of this method, the test experiment with a mock-up (flat board and reactor) system is performed. The experimental results show the efficiency of this algorithm even under high level noise and potential application to Loose Part Monitoring System (LPMS) for improving diagnosis capability in nuclear power plants.

• 한국산학기술학회논문지
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• 제13권8호
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• pp.3779-3784
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• 2012

산업체 공정의 실시간 공정 모니터링과 진단은 생산 제품의 품질과 안전을 보장하는데 반드시 필요한 활동들의 하나이다. 그중에서 공정 진단은 공정에 발생된 특정 이상상황의 원인을 밝혀내는 것으로서 조업자들이 이상상황의 근본원인을 보다 효과적으로 도출하는데 도움을 줄 수 있다. 본 논문에서는 비선형 KFDA 기법과 데이터 전처리기법을 이용한 이상원인 진단방법을 적용하고 이의 진단 성능을 기존 선형 기법에 기반한 PCA 진단방법과 비교한다. 실제 공정을 모사한 Tennessee Eastman 공정 시뮬레이터의 공정 데이터를 통한 사례연구를 수행한 결과 기존 선형 진단 방법론 대비 신뢰할 수 있는 진단 결과를 얻을 수 있었다.

• 설비공학논문집
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• 제20권10호
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• pp.669-678
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• 2008

When operating a complex facility, Fault Detection and Diagnosis (FDD) system is beneficial in equipment management by providing the operator with tools which can help find out a failure of the system. In this research, FDD algorithm was developed using the general pattern classifier method that can be applied to centrifugal chiller system. The simulation model for a centrifugal chiller system was developed in order to obtain characteristic data of turbo chiller system under normal and faulty operation. We tested FDD algorithm of a centrifugal chiller using data from simulation model at full load performance and 60% part load performance. In this research, we presented fault detection method using a normalized distance. Sensitivity analysis of fault detection was carried out with respect to fault progress. FDD algorithm developed in this study was found to indicate each failure modes accurately.

• 한국전자통신학회논문지
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• 제17권5호
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• pp.809-814
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• 2022

주의력결핍장애는 산만함, 과잉행동을 보여주는 질환으로 소아청소년기에 진단 시 성인까지 증상이 지속되기 때문에 조기에 진단 및 치료를 시작하는 것이 중요하다. 그러나 소아는 정신적으로 미성숙하기 때문에 자가진단법 또는 측정 장비를 이용할 때 올바른 진단 데이터를 획득하기 어려운 문제가 있다. 이 논문에서는 ADHD 진단의 객관성과 정확도를 높이기 위하여 게임 콘텐츠를 이용하여 측정된 뇌전도 데이터에 대하여 CNN 모델링을 기반으로 분류하는 기법을 제시하고 실험하였다. 실험을 위하여 3D 네트워크 모델을 구성하였으며 평균적으로 97%의 정확도를 보여주었다.

• 한국전기전자재료학회논문지
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• 제14권1호
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• pp.84-92
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• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

• 한국철도학회논문집
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• 제2권2호
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• pp.13-20
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• 1999

The vibration signals of driving parts of electric train are distorted its signal patterns due to the impact components, which occurs when wheel passes rail joints. An elimination method of the impact components is investigated using adaptive signal processing technique in this study The result shows that adaptive interference canceling method seems to be more effective than line enhancement technique. The application of adaptive interference canceling method to the signal measured at bogie shows that the extractions of the signals of driving parts of traction motor, reduction gear, and axle bearing are successful. Therefore, only the signals of bogie, which is the place to attach an accelerometer easily, is sufficient for the fault diagnosis and the safety evaluation of electric train. Also, adaptive interference canceling method can be applicable to evaluate the performance of vibration isolation between bogie and car body and to investigate the characteristics of indoor sound.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 하계학술대회 논문집
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• pp.459-462
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• 2002

The conventional thermal insulator and power transformer testing is widely used in surface aging measurement of outside insulator because those testing can carry out very short time in Lab testing. Also thermal testing is able to offer the standard judgement of relative degradation level of outside HV machine. There it is very useful method compare to previous conventional thermal testing method and effective Lab testing method. But surface discharges(SD) have very complex characteristics of discharge pattern so it is required estimation research to development of precise analysis method. In recent, the study of IRR-camera is carrying out discover of temperature of power equipment through condition diagnosis and system development of degradation diagnosis. In this study, thermal testing of Power transformer is measured with partial temperature distribution in real time.

• 제어로봇시스템학회논문지
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• 제18권6호
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• pp.540-545
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• 2012

In this paper, we propose a fault diagnosis method using artificial neural network and SVM (Support Vector Machine) to detect and isolate faults in the nonlinear systems. The proposed algorithm consists of two main parts: fault detection through threshold testing using a artificial neural network and fault isolation by SVM fault classifier. In the proposed method a fault is detected when the errors between the actual system output and the artificial neural network nominal system output cross a predetermined threshold. Once a fault in the nonlinear system is detected the SVM fault classifier isolates the fault. The computer simulation results demonstrate the effectiveness of the proposed SVM and artificial neural network based fault diagnosis method.