• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.3
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• pp.384-390
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• 2012

This paper proposes a fault diagnosis method of the line-to-line voltage sensors in 3-phase AC/DC pulse width modulation (PWM) converters. The line-to-line voltage sensors are an essential device to obtain the information of the grid voltages for controlling the 3-phase AC/DC PWM converters. If the line-to-line voltage sensors are mismeasured by various faults, the voltage sensors can obtain wrong information of the grid voltage. It has an adverse effect on the control of the converter. Therefore, the converter causes the unbalance input AC current and the DC-link voltage ripple in the 3-phase AC/DC PWM converter. Hence, fast fault detection and fault tolerant control are needed. In this paper, the fault diagnosis method is proposed and verified through simulations and experiments.

• Proceedings of the KIEE Conference
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• 2005.07e
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• pp.31-33
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• 2005

The accelerating of industrial development causes electricity demand to increase. By that power equipments need high power, multi function and intelligence. Also consumers demand for guarantee power supplying of good quality and reasonable operating equipment. Also they require for reliance and stabilization of power facility. Therefore preventive maintenance of electric installation must be developed and improvement of domestic technical level is needed in the maintenance management of equipment. The diagnosis of trouble existence is technique that compares steady state with unusual condition, whereas the prediction technique makes a diagnosis of remaining equipments life. It is difficult for us to diagnose trouble existence of electric installation and to develop prediction method in building because of a wide scope for electric installation in building. And in this paper we will investigate diagnosis and prediction method for only switch part of electric installation in building.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.1-8
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• 2004

In this paper, we propose neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, ART2 neural network is used for fault isolation. The performance and effectiveness of the proposed ART2 neural network based fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

• Journal of Electrical Engineering and Technology
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• v.2 no.2
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• pp.157-164
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• 2007

Diagnosis techniques based on the dissolved gas analysis(DGA) have been developed to detect incipient faults in power transformers. Various methods exist based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied to different problems with different standards. Furthermore, it is difficult to achieve an accurate diagnosis by DGA without experienced experts. In order to resolve these drawbacks, this paper proposes a novel diagnosis method using fuzzy clustering and a radial basis neural network(RBFNN). In the neural network, fuzzy clustering is effective for selecting the efficient training data and reducing learning process time. After fuzzy clustering, the RBF neural network is developed to analyze and diagnose the state of the transformer. The proposed method measures the possibility and degree of aging as well as the faults occurred in the transformer. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.221-225
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• 2023

In this paper, we proposed an efficient method for rapid diagnosis of COVID-19 by voice. A novel Strided Convolution Separable Transformer (SC-SepTr) is proposed by modifying the conventional Separable Transformer (SepTr) for audio signal recognition. The proposed method reduces the memory and computational requirements to enable rapid diagnosis of COVID-19. As a result of experiments on Coswara, it was shown that the proposed method perform rapid diagnosis with guaranteeing Area Under the Curve (AUC) performance even for a relatively small amount of learning data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6502-6508
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• 2014

The concept of Communities of Practice (CoPs) has been highlighted as an effective method for knowledge sharing in Knowledge Management (KM) and has been utilized strategically by many organizations. Therefore, the need to diagnose knowledge sharing activities in CoPs has increased. Previous studies of CoP strategies has generally suggested broad guidelines without diagnosing the current knowledge sharing status of individual CoPs. Furthermore, diagnosis methodologies are not connected to the strategic direction and require considerable time and effort to conduct regularly. The purpose of this paper was to develop a sustainable diagnosis framework for identifying knowledge sharing activities in virtual CoPs and to suggest strategies for CoPs-based on the proposed diagnosis framework. Finally, the proposed diagnosis framework was applied to an educational service case.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.3
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• pp.143-147
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• 2005

Monitoring and diagnosis of the operating machines are very important for safety operation and maintenance in the industrial fields. These machines are most rotating machines and the diagnosis of the machines has been researched for long time. We can easily see the faulted signal of the rotating machines from the changes of the signals in frequency. The Linear Predictive Coding(LPC) is introduced for signal analysis in frequency domain. In this paper, we propose fault detection and diagnosis method using the Linear Predictive Coding(LPC) and residual signal energy. We applied our method to the induction motors depending on various status of faulted condition and could obtain good results.

• Journal of Digital Contents Society
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• v.10 no.3
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• pp.441-446
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• 2009

This study suggests a reliable risk diagnosis system on the lease deposit, as one of main functions for a real estate information system. A previous system adopted a method where a user should input all required data and the program just performs a simple calculation to provide the results to users. Such a methodology makes a user feel uncomfortable and reduces the reliability for the risk diagnosis of the lease deposit. Therefore, the suggested method in this paper is to minimize the data input by users and to provide a proper sale price to users based on the existing raw data and the statistic court auction data. In addition to the risk diagnosis, it explains about some possible risk information and provides a way to control a risk so that a user can recognize any risk.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2134-2143
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• 2018

This work introduces a fault diagnosis method for transformer based on self-powered radio frequency identification (RFID) sensor tag and improved Hilbert-Huang transform (HHT). Consisted by RFID tag chip, power management circuit, MCU and accelerometer, the developed RFID sensor tag is used to acquire and wirelessly transmit the vibration signal. A customized power management including solar panel, low dropout (LDO) voltage regulator, supercapacitor and corresponding charging circuit is presented to guarantee constant DC power for the sensor tag. An improved band restricted empirical mode decomposition (BREMD) which is optimized by quantum-behaved particle swarm optimization (QPSO) algorithm is proposed to deal with the raw vibration signal. Compared with traditional methods, this improved BREMD method shows great superiority in reducing mode aliasing. Then, a promising fault diagnosis approach on the basis of Hilbert marginal spectrum variations is brought up. The measured results show that the presented power management circuit can generate 2.5V DC voltage for the rest of the sensor tag. The developed sensor tag can achieve a reliable communication distance of 17.8m in the test environment. Furthermore, the measurement results indicate the promising performance of fault diagnosis for transformer.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.506-508
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• 2007

In this paper, the induction motor bearing fault diagnosis system using current signals which are measured by over-sampling method is presented. In the case of inverter fed motor drive unlike line-driven motor drive, that make a lot of noise which can cause a wrong fault signals because of PWM(pulse width modulation) voltage. So, the current signals for fault diagnosis need very precise and high resolution information, which means this system demand additional hardware such as low pass filter, high resolution ADC system and so on to use fault diagnosis system. Therefore, the proposed over-sampling method is expected to contribute to low cost fault diagnosis system even though previous inverter fed motor drive without any additional hardware. In order to confirm the presented algorithms, various experiments for bearing faults are tested and the line current spectrum of each faulty situation using park transformation is compared with a FFT results.