• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1614-1622
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• 2018

The research related to fault diagnosis in permanent magnet synchronous motors (PMSMs) has attracted considerable attention in recent years because various faults such as permanent magnet demagnetization and short-circuited turns can occur and result in unexpected failure of motor related system. Several conventional current and back electromotive force (BEMF) analysis techniques were proposed to detect certain faults in PMSMs; however, they generally deal with a single fault only. On the contrary, cases of multiple faults are common in PMSMs. We propose a fault diagnosis method for PMSMs with single and multiple combined faults. Our method uses three phase BEMF voltages based on the fast Fourier transform (FFT), support vector machine(SVM), and visualization tools for identifying fault types and severities in PMSMs. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to visualize the high-dimensional data into two-dimensional space. Experimental results show good visualization performance and high classification accuracy to identify fault types and severities for single and multiple faults in PMSMs.

• 제어로봇시스템학회논문지
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• 제21권4호
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• pp.315-320
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• 2015

This paper describes the implemenation of localization and visualization scheme to find out an ultrasonic source caused by defects of a power distribution line equipment. To increase the fault detection performance, $2{\times}4$ sensor array is configured with MEMS ultrasonic sensors, and from the sensor signals aquired, the azimuth and elevation angles of the ultrasonic source is estimated based on the delay-sum beam forming method. Also, to visualize the estimated location, it is marked on the background image. Experimental results show applicability of the present technique.

• 한국소음진동공학회논문집
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• 제21권3호
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• pp.199-205
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• 2011

This paper proposes a framework to predict the mechanical fault of hot rolling mill system (HRMS). The optimum process of HRMS is usually identified by the rotating velocity of working roll. Therefore, observing the velocity of working roll is relevant to early know the HRMS condition. In this paper, we propose the framework which consists of two methods namely spectrum matrix which related to case-based fast Fourier transform(FFT) analysis, and three dimensional condition monitoring based on novel visualization. Validation of the proposed method has been conducted using vibration data acquired from HRMS by accelerometer sensors. The acquired data was also tested by developed software referred as hot rolling mill facility analysis module. The result is plausible and promising, and the developed software will be enhanced to be capable in prediction of remaining useful life of HRMS.

• 한국가스학회:학술대회논문집
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• 한국가스학회 2006년도 추계학술발표회 논문집
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• pp.235-240
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• 2006

• Structural Engineering and Mechanics
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• 제87권5호
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• pp.431-450
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• 2023

In this study, it is intended to perform nonlinear time-history analyses of nuclear power plant structures (NPP) under near-fault earthquakes showing directivity pulse and fling-step characteristics. Simulation procedures based on cycloidal pulse and far-fault ground motions are also used to simulate near-fault motions showing forward-directivity and fling-step characteristics and the structural responses are compared with those of the recorded near-fault ground motions. Because it is aimed to determine specifically the pulse type characteristics of near-fault ground motions on NPPs, all the ground motions are normalized to have a PGA of 0.3 g. Depending on the obtained results it can be underlined that although near-fault ground motion has the potential to cause damage mostly on structural systems having larger periods, it may also have noticeable effects on the responses of rigid structures, like NPP containment buildings. On the other hand, simulated near-fault motions can help us to get an insight into the near-fault mechanism as well as an approximate visualization of the structural responses under near-fault earthquakes.

• 대한전기학회논문지:전력기술부문A
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• 제55권7호
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• pp.297-305
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• 2006

This paper summarizes a development of visualization software for protective engineering in low-voltage power systems. The study is concentrated on the following aspects. First, a software engineering method is applied for designing the object-oriented program. The design and implementation of a Graphic User Interface(GUI) and its integration to a power system framework are developed using object-oriented programming(OOP) in Visual C++. Second, we develop the short circuit analysis module that oriented a low-voltage power system. It is possible to calculate a peak, symmetrical RMS, DC component and asymmetrical fault currents for each time. And it is the first software that can calculate the fault current for single branch of three-phase system. The calculation accuracy is compared with commercial software, and the libraries of low-voltage components are served for convenience use. Third, protective engineering functions are equipped. It is possible to automatically select the circuit breaker which based on the user input characteristics and the fault current calculation and examine the protective coordination. Through the case study, we verified that the developed software can be effectively used to examine the protective engineering in low-voltage power systems.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• 한국정보통신학회논문지
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• 제26권1호
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• pp.146-152
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• 2022

라즈베리 파이나 아두이노를 이용하여 온도, 습도, 기울기, 압력 센서 등에서 수집한 정보는 자동 항온, 항습 시스템 등에 활용되고 있다. 또 농축산업에 활용하여 원격에서 스마트폰만으로 시스템을 제어함으로써 농축산업 종사자들이 편리하게 활용하기도 한다. 일반적으로 온도, 습도는 꺾은선 그래프 등으로 표현하여 변화를 실시간으로 감시하는 역할을 수행하기도 한다. 온도를 시각적으로 표현하는 기술은 최근 코로나19의 발열 검사를 위해 적외선 기기를 이용해 직관적으로 활용되기도 한다. 본 논문에서는 라즈베리 파이와 DHT11 센서에서 수집된 정보를 직관적인 시각화를 통해 공간상의 온도변화를 예측하고 즉각적인 대응이 가능하도록 제안한다. 이를 위해 온도, 습도를 효과적으로 시각화하기 위해 알고리즘을 만들었고, 일부 센서에 결함이 발생하는 경우에도 데이터 표현이 가능하다.

• 한국안전학회지
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• 제15권2호
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• pp.36-45
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• 2000

Automated production system is composed of many complicated techniques and it become a very difficult task to control, monitor and diagnose this compound system. Moreover, it is required to develop an effective diagnosing technique and reduce the diagnosing time while operating the system in parallel under many faults occurring concurrently. This study develops a Modular Artificial Neural Network(MANN) which can perform a diagnosing function of multiple faults with the following steps: 1) Modularizing a complicated system into subsystems. 2) Formulating a hierarchical structure by dividing the subsystem into many detailed elements. 3) Planting an artificial neural network into hierarchical module. The system developed is implemented on workstation platform with $X-Windows^{(r)}$ which provides multi-process, multi-tasking and IPC facilities for visualization of transaction, by applying the software written in $ANSI-C^{(r)}$ together with $MOTIF^{(r)}$ on the fault diagnosis of PI feedback controller reactor. It can be used as a simple stepping stone towards a perfect multiple diagnosing system covering with various industrial applications, and further provides an economical approach to prevent a disastrous failure of huge complicated systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.518-520
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• 2004

In these days, there is a demand to develop fault current limiters(FCLs) to reduce excessive fault current and protect electrical equipments which are installed in the transmission and distribution power systems. We considered the resistive superconducting FCLs among the various kinds of FCLs. In this study, in order to develop the resistive superconducting FCL of 6.6kV 200A $3\phi$, we designed the new mask pattern for etching YBCO films by means of numerical analysis method, current limiting experiments and visualization of bubbles in films and investigated dielectric performance of the designed mask by using elecrtostatic numerical analysis method and breakdown experiments. We etched YBCO films by using the newly designed mask, connected the etched films in series and in parallel, and designed the 6.6kV resistive SFCL and then we observed the current limiting characteristics of the SFCL.