• Journal of Electrical Engineering and Technology
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• 제8권4호
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• pp.813-823
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• 2013

On-line monitoring system is important for high voltage vacuum circuit breakers (HVCBs) in operation condition assessment and fault diagnosis. A distributed multilayer system with client/server architecture is developed on rated voltage 10kV HVCB with spring operating mechanism. It can collect data when HVCB switches, calculate the necessary parameters, show the operation conditions and provide abundant information for fault diagnosis. Ensemble empirical mode decomposition (EEMD) is used to detect the singular point which is regarded as the contact moment. This method has been applied to on-line monitoring system successfully and its satisfactory effect has been proved through experiments. SVM and FCM are both effective methods for fault diagnosis. A combinative algorithm is designed to judge the faults of HVCB's operating mechanism. The system's precision and stability are confirmed by field tests.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2175-2177
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• 2001

This paper presents a faults diagnosis technique of induction motors based on a neural network. Only stator current is measured, transformed by using FFT and normalized for the training. Healthy, bearing fault, stator fault and rotor end-ring fault motors are prepared to obtain the learning data and diagnose the several faults. For more effective diagnosis, the load rate is changed by 100%, 60%, 30% of full load and the obtained are applied to the learning process. The experimental results show the proposed method is very detectable and applicable to the real diagnosis system.

• Journal of information and communication convergence engineering
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• 제20권1호
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• pp.58-64
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• 2022

Breast ultrasonic reading is critical as a primary screening test for the early diagnosis of breast cancer. However, breast ultrasound examinations show significant differences in diagnosis based on the difference in image quality according to the ultrasonic equipment, experience, and proficiency of the examiner. Accordingly, studies are being actively conducted to analyze the texture characteristics of normal breast tissue, positive tumors, and malignant tumors using breast ultrasonography and to use them for computer-assisted diagnosis. In this study, breast ultrasonography was conducted to select 247 ultrasound images of 71 normal breast tissues, 87 fibroadenomas among benign tumors, and 89 malignant tumors. The selected images were calculated using a statistical method with 21 feature parameters extracted using the gray level co-occurrence matrix algorithm, and classified as normal breast tissue, benign tumor, and malignancy. In addition, we proposed five feature parameters that are available for computer-aided diagnosis of breast cancer classification. The average classification rate for normal breast tissue, benign tumors, and malignant tumors, using this feature parameter, was 82.8%.

• Journal of information and communication convergence engineering
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• 제20권1호
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• pp.31-40
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• 2022

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.

• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.43-48
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• 2023

This paper discusses the design of 32Mbyte asynchronous nonvolatile memory modules, which includes self-diagnosis and RTC (Real Time Clock) functions to enhance their data stability and reliability. Nonvolatile memory modules can maintain data even in a power-off state, thereby improving the stability and reliability of a system or device. However, due to the possibility of data error due to electrical or physical reasons, additional data loss prevention methods are required. To minimize data error in asynchronous nonvolatile memory modules, this paper proposes the use of voltage monitoring circuits, self-diagnosis, BBT (Bad Block Table), ECC (Error Correction Code), CRC (Cyclic Redundancy Check)32, and data check sum, data recording method using RTC. Prototypes have been produced to confirm correct operation and suggest the possibility of commercialization.

• 드라이브 ㆍ 컨트롤
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• 제20권4호
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• pp.54-63
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• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

• 비파괴검사학회지
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• 제31권2호
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• pp.134-138
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• 2011

회전기기의 고장 탐지에 있어 기존의 진단법과 달리 동적 하중조건 하에서 비접촉, 비파괴의 적외선 열화상 기법이 제안된다. 본 논문에서는 단열 깊은 홈 볼 베어링을 시편으로 하여, 회전기기의 기존의 고장 진단법 대신 수동형 열화상 기법을 이용한 시험을 수행하였다. 추가적으로, 제안된 방법의 효율성을 평가하기 위해 기존의 진동 스펙트럼 분석법을 적용하여 열화상 시험법을 비교하였다. 시혐의 결과로써, 동적 하중조건 하 볼 베어령의 온도분포 특성이 철저히 분석되었다.

• 조명전기설비학회논문지
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• 제18권4호
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• pp.104-111
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• 2004

본 논문에서는 산업전반에 걸쳐 널리 사용되는 유도전동기의 고장상태를 검출하기 위해 선형판별분석기법에 기반을 둔 진단 알고리즘을 제안하고자 한다. 제안된 기법은 우선 주기별로 실험에 의해 측정된 전류값의 입력차원을 주성분분석기법을 이용하여 축소한 후 선형판별분석기법을 이용하여 고장상태별로 특징벡터를 추출한다. 다음으로 진단단계는 확보된 고장 종류별 특징벡터와 운전 시 입력되는 특징벡터간의 유클리디안 거리를 이용하여 유도전동기의 운전상태를 진단하는 구조로 되어있다. 마지막으로 선형판별분석기법의 타당성을 보이기 위해 노이즈가 있는 다양한 조건하에서 실험한 결과, 주성분분석기법만을 이용한 경우보다 우수한 결과를 나타냈다.

• 한국산학기술학회논문지
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• 제10권10호
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• pp.2741-2749
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• 2009

스마트 홈에서 발생하는 다양한 형태의 오류는 스마트 홈의 신뢰성을 저하시키기 때문에 스마트 홈에서 오류의 검출 및 복구를 위한 연구가 그 동안 진행되어 왔으나, 이들 대부분은 장치의 기능적 고장이나 소프트웨어의 오동작 등에 한정되어 있고, 장치간의 연관 관계에서 발생하는 오류에 대한 것은 없었다. 본 논문에서는 장치간의 연관 관계를 규칙으로 정의하고, 규칙의 만족 여부에 따라 컨텍스트를 두 집합으로 구분한 다음, 장치간의 연관 관계에서 발생하는 오류의 증상과 원인을 정의하는 오류 진단 지식 생성 방법을 제시한다. 향후, 스마트 홈에 적용하여 이 방법을 장치들의 연관성에 의해 발생하는 오류의 탐지와 그 원인의 식별이 실시간으로 가능하다.

• Advances in aircraft and spacecraft science
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• 제7권1호
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• pp.1-17
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• 2020

In this paper, a new model-based Fault Detection and Diagnosis (FDD) method for an agile supersonic flight vehicle is presented. A nonlinear model, controlled by a classical closed loop controller and proportional navigation guidance in interception scenario, describes the behavior of the vehicle. The proposed FDD method employs the Inertial Navigation System (INS) data and nonlinear dynamic model of the vehicle to inform fins damage to the controller before leading to an undesired performance or mission failure. Broken, burnt, unactuated or not opened control surfaces cause a drastic change in aerodynamic coefficients and consequently in the dynamic model. Therefore, in addition to the changes in the control forces and moments, system dynamics will change too, leading to the failure detection process being encountered with difficulty. To this purpose, an equivalent aerodynamic model is proposed to express the dynamics of the vehicle, and the health of each fin is monitored by the value of a parameter which is estimated using an adaptive robust filter. The proposed method detects and isolates fins damages in a few seconds with good accuracy.