• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1057-1058
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• 2007

This paper proposes an alternative vibration detection method in a squirrel-cage induction motor using flux sensors. The air-gap flux will be changed when mechanical vibration occurs by bearing fault as well as broken rotor bar and air-gap eccentricity. For detecting those flux variations due to vibration, search coils are installed at stator slots. The induction motor with 380 [V], 7.5 [kW], 4 [Poles], 1,760 [rpm] ratings is used. Magnitudes and distortion of the induced voltage from flux sensors are used to discriminate faulted types. As a result, the flux sensor has been proven to be useful for vibration detection. It is compared to the result with vibration sensor as well.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.2052-2053
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• 2007

Recently, the importance of supplying the reliable electric power is increasing. Breaking insulation of stator winding is major cause of fault in rotator machine. On-line PD detecting is useful technique to diagnose rotator machine. However, interpretation of its results in time domain is very complex because of the mixed results with PD(Partial Discharge) and noise signal. Therefore, the results were analyzed in frequency domain by FFT (Fast Fourier Transform) to detect precise PD signals. The purpose of this paper is to describe the optimal frequency range to discriminate the PD and noise signal.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.95-96
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• 2007

Dissolved gas analysis (DGA) is one of the most widely used diagnostic tools for detecting and evaluating faults in electrical equipment. However, interpretation of DGA results is often complex and should always be done with care, involving experienced insulation maintenance personnel. KEPCO (Korea Electric Power Cooperation) has been using DGA technique since KEPCO established the criteria of DGA in 1985. In this paper, we introduce the DGA criteria of KEPCO and analyze the result of DGA. Also we sort pattern in result of DGA. Then, relation between pattern and inner inspection was studied. 67 DGA data was used for analyzing pattern. Some patterns have something to do with cause of incipient fault.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.123.2-123.2
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• 2013

Etching endpoint detection with plasma impedance monitoring (PIM) is demonstrated for small area dielectric layers inductive coupled plasma etching. The endpoint is determined by the impedance harmonic signals variation from the I-V monitoring system. Measuring plasma impedance has been examined as a relatively simple method of detecting variations in plasma and surface conditions without contamination at low cost. Cluster analysis algorithm is modified and applied to real-time endpoint detection for sensitivity enhancement in this work. For verification, the detected endpoint by PIM and real-time cluster analysis is compared with widely used optical emission spectroscopy (OES) signals. The proposed technique shows clear improvement of sensitivity with significant noise reduction when it is compared with OES signals. This technique is expected to be applied to various plasma monitoring applications including fault detections as well as end point detection.

• Geosciences Journal
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• v.22 no.6
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• pp.891-902
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• 2018

Techniques for more reliable detection of 3D subsurface flow paths are highly important for most water-related geotechnical projects. In this case study, a magnetometric resistivity method with a new approach and state-of-the-art technology ("Willowstick survey") was applied to the testbed dam (YD dam) site, and its applicability was validated by geotechnical investigation techniques including borehole drilling and sampling, Lugeon test, flow direction and velocity test, and seismic tomography. In addition to the magnetometric survey, a 3D electrical resistivity survey was performed independently and the results were compared and discussed. The electrical resistivity survey was effective in detecting groundwater levels, but it was limited in mapping leakage paths. On the other hand, the Willowstick magnetometric survey effectively detected geologic weaknesses (e.g., fault fracture) and potential leakage paths of the dam site foundation rocks. The results of this research are expected to be effective for water infrastructures where leakage is an important issue.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.97-104
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• 2008

Most of engine control systems for helicopter turboshaft engines are equipped with dual sensors. For the system with dual redundancy, analytic methods are used to detect faults based on the system dynamical model. Helicopter engine dynamics are affected by aerodynamic torque induced from the dynamics of the main rotor. In this paper an engine model including the rotor dynamics is constructed for the T700-GE-700 turboshaft engine powering UH-60 helicopter. The singular value decomposition(SVD) method is applied to the developed model in order to detect sensor faults. The SVD method which do not need an additional computation to generate residual uses the characteristics that the system outputs in direction of the left singular vector if an input is applied in direction of the right singular vector. Simulations show that the SVD method works well in detecting and isolating the sensor faults.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.305-312
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• 2018

Software has been used to develop many functions of the modern weapon systems which has a high mission criticality. Weapon system software must consider multi-threaded processing to satisfy growing performance requirement. However, developing multi-threaded programs are difficult because of concurrency faults, such as unintended data races. Especially, it is important to prepare analysis for debugging the data races, because the weapon system software may cause personal injury. In this paper, we present an efficient framework of analysis, called ConDeWS, which is designed to determine the scope of dynamic analysis through using the result of static analysis and fault analysis. As a result of applying the implemented framework to the target software, we have detected unintended data races that were not detected in the static analysis.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.3
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• pp.15-23
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• 2019

This paper contains analyses and development processes of the requirements to detect the possible intermittent failure in an old avionics backplane. Interconnections for signal transmission between electronic components, such as Pin-to-PCB, FPCB-to-FPCB, pin-to-FPCB, and pint-to-wire, were selected as the main cause of intermittent failure by analyzing target equipment and documents. The possibility of detecting intermittent failures occurring in the target equipment is verified by physics-of-failure analyses. In order to verify the occurrence of intermittent failures and their detectability, latching continuity circuit testers were manufactured and accelerated life tests were performed by applying temperature and vibration cycle in consideration of flight conditions. Through the above process, the detection requirements for the major intermittent failure in the target avionics backplane was developed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.581-583
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• 2021

Due to the pendemic of Corona 19, the use of mobile services is increasing. However, since anomalies in most mobile devices are recognized by the device's alarm, it is difficult to intuitively determine the problem of the device when a complex failure occurs. To compensate for this, in this study, the Anomaly Score was created by RRCF algorithm to intuitively recognize the problem by combining the alarm and performance information of the equipment, and the effect of detecting 97% of the past failure history was verified.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.