• Nuclear Engineering and Technology
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• 제55권3호
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• pp.827-838
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• 2023

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

• 조명전기설비학회논문지
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• 제23권4호
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• pp.62-71
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• 2009

본 논문에서는 주성분 분석 기법에 의한 BLDC 전동기의 고장진단 알고리즘과 임베디드 타입의 실시간 고장진단 시스템을 구현하였다. 우선 오프라인 상태에서 제안된 고장진단 알고리즘을 검증하기 위해 BLDC 고장진단 실험장치를 구현한 후 LabVIEW 프로그램에 의해 다양한 고장 데이터를 취득하였다. 취득된 데이터는 신호특성에 맞는 전 처리과정을 수행한 후 주성분분석 기법에 의해 고장특성을 나타내는 특징을 추출하고 최종적으로 BLDC 전동기의 진단은 유클리디안 거리 유사도 방법에 의해 수행된다. 이러한 결과를 바탕으로 임베디드 타입의 실시간 BLDC 고장진단 시스템을 구현하였다. 제안된 방법은 다양한 실험을 통하여 성능을 평가하였다.

• 전기학회논문지
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• 제65권6호
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• pp.1108-1114
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• 2016

The cause of failure in the AT feeding system is divided into grounding, short-circuit of feeding circuit and internal faults of the railway substation. Since the fault current is very high, real-time current is detected and the failure must be immediately removed. In this paper, a new DC offset elimination filter that can remove component to decrease in the form of exponential function using low-pass filter was proposed in order to extract the fundamental wave from distorted fault current. In order to confirm the performance of the proposed filter method, AT feeder system was modelled by simulation tool and simulations were performed under various conditions such as fault location, fault resistance and fault voltage phase angle in case of trolley-rail short-circuit fault. When applying the proposed DC-offset removal method, it can be seen that the phase delay and gain error did not appear.

• 대한기계학회논문집A
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• 제27권4호
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• pp.537-543
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• 2003

Transient vibration generated by developing localized fault in gear can be used as indicators in gear fault detection. This vibration signal suffers from the background noise such as gear meshing frequency and its harmonics and broadband noise. Thus in order to extract the information about the only gear fault from the raw vibration signal measured on the gearbox this signal is processed to reduce the background noise with many kinds of signal-processing tools. However, these signal-processing tools are often very complex and time waste. Thus. in this paper. we propose a novel approach detecting the damage of gearbox and analyzing its pattern using the raw vibration signal. In order to do this, the residual signal. which consists of the sideband components of the gear meshing frequent) and its harmonics frequencies, is extracted from the raw signal by the power spectral density (PSD) to obtain the information about the fault and is used as the input data of the artificial neural network (ANN) for analysis of the pattern of gear fault. This novel approach has been very successfully applied to the damage analysis of a laboratory gearbox.

• 정보보호학회논문지
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• 제22권5호
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• pp.1009-1017
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• 2012

비밀 키가 내장된 암호 장치에 대한 오류 주입 공격은 공격자가 암호화 연산 시 오류를 주입하여 암호 시스템의 키를 찾아내는 공격이다. 이 공격은 AES와 같은 암호 시스템에서 한 바이트의 오류 주입으로도 비밀 키 전체를 찾아낼 수 있을 정도로 매우 위협적이다. 본 논문에서는 AES 암호 시스템에서 입 출력값의 차분을 검사하는 방법으로 오류 주입 공격을 방어하는 새로운 오류 검출 기법을 제안한다. 또한, 제안 방법이 기존의 공격 대응 방법들과 비교하여 오류 탐지 능력이 우수하고 구현에 필요한 추가적인 오버헤드가 적어 효율적임을 컴퓨터 시뮬레이션을 통해 확인하였다.

• 한국지형학회지
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• 제26권2호
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• pp.69-84
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• 2019

The purpose of this study is to extract lineaments in the southeastern part of the Gaeum Fault System, and to understand their characteristics and a relationship between them and fault activation. The lineaments were extracted using a multi-layered analysis based on a digital elevation model (5 m resolution), aerial photos, and satellite images. First-grade lineaments inferred as an high-activity along them were classified based on the displacement of the Quaternary deposits and the distribution of fault-related landforms. The results of classifying the first-grade lineaments were verified by fieldwork and electrical resistivity survey. In the study area of 510 km², a total of 222 lineaments was identified, and their total length was 333.4 km. Six grade lineaments were identified, and their total length was 11.2 km. The lineaments showed high-density distribution in the region along the Geumcheon, Gaeum, Ubo fault, and a boundary of the Hwasan cauldron consisting the Gaeum Fault System. They generally have WNW-ESE trend, which is the same direction with the strike of Gaeum Fault System. Electrical resistivity survey was conducted on eight survey lines crossing the first-grade lineament. A low-resistivity zone, which is assumed to be a fault damage zone, has been identified across almost all survey lines (except for only one survey line). The visual (naked eyes) detecting of the lineament was evaluated to be less objectivity than the automatic extraction using the algorithm. However, the results of electrical resistivity survey showed that first-grade lineament extracted by visual detecting was 83% reliable for inferred fault detection. These results showed that objective visual detection results can be derived from multi-layered analysis based on tectonic geomorphology.

• 한국소음진동공학회논문집
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• 제20권11호
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• pp.1009-1017
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• 2010

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying various techniques such as signal processing and pattern recognition. Recently, fault diagnosis systems using artificial neural network have been proposed. For effective fault diagnosis, this paper used MLP(multi-layer perceptron) network which is widely used in pattern classification. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes the decision method of the proper feature vectors about each fault signal for neural-network-based fault diagnosis system. We applied LPC coefficients, maximum magnitudes of each spectral section in FFT and RMS(root mean square) and variance of wavelet coefficients as feature vectors and selected appropriate feature vectors as comparing error ratios of fault diagnosis for sound, vibration and current fault signals. From experiment results, LPC coefficients and maximum magnitudes of each spectral section showed 100 % diagnosis ratios for each fault and the method using wavelet coefficients had noise-robust characteristic.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.9-11
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• 2006

This paper suggests a revision method for historical fault data using Proportional Aging Reduction(PAR) to consider maintenance effect in time-varying failure rate. In order to product time-varying failure rate, the historical fault data are necessary. However, the maintenance record could be left out in historical data by spot operator's mistake. In this case, the failure rate is produced less than the average failure rate for increasing equipments' life-time by maintenance effect. Hence, it is necessary for new time-varying failure rate to extract maintenance effect from the existing fault data. In this paper, the revision method to reduce equipments' life-time, adversely using PAR among three techniques to consider maintenance effect.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 춘계학술발표대회
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• pp.562-565
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• 2018

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2485-2492
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• 2018

The rapid development of large-scale integrated circuits has brought great challenges to the circuit testing and diagnosis, and due to the lack of exact fault models, inaccurate analog components tolerance, and some nonlinear factors, the analog circuit fault diagnosis is still regarded as an extremely difficult problem. To cope with the problem that it's difficult to extract fault features effectively from masses of original data of the nonlinear continuous analog circuit output signal, a novel approach of feature extraction and dimension reduction for analog circuit fault diagnosis based on wavelet packet decomposition, local linear embedding algorithm, and clone selection algorithm (WPD-LLE-CSA) is proposed. The proposed method can identify faulty components in complicated analog circuits with a high accuracy above 99%. Compared with the existing feature extraction methods, the proposed method can significantly reduce the quantity of features with less time spent under the premise of maintaining a high level of diagnosing rate, and also the ratio of dimensionality reduction was discussed. Several groups of experiments are conducted to demonstrate the efficiency of the proposed method.