• Journal of IKEEE
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• v.22 no.4
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• pp.941-947
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• 2018

In general, capacitors have a large influence on the life of the system due to frequent charging and discharging. In this paper, we analyze the cause of the core failure of high voltage, high current HVDC sub-module film capacitor and analyze the precautions of the capacitor design and manufacturing process. First, the cause of the fault, the failure mode, and the effect are analyzed through the FMEA of the capacitor. To quantitatively evaluate the causes and effects of faults that have the greatest effect on the failure of a capacitor, a fault tree for the capacitor is presented and the failure rate is analyzed according to the design parameters and the driving conditions. It is verified that the main cause of capacitor failure is the capacitance change, and it is necessary to minimize the temperature rise, corona occurrence, electrode expansion, and insulation distance decrease during capacitor design and manufacturing process in order to reduce the failure rate of the capacitor.

• Structural Engineering and Mechanics
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• v.76 no.6
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• pp.751-763
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• 2020

Available records of recent earthquakes show that near-field earthquakes have different characteristics than far-field earthquakes. In general, most of these unique characteristics of near-fault records can be attributed to their forward directivity. This phenomenon causes the records of ground motion normal to the fault to entail pulses with long periods in the velocity time history. The energy of the earthquake is almost accumulated in these pulses causing large displacements and, accordingly, severe damages in the building. Damage to structures caused by past earthquakes raises the need to assess the chance of future earthquake damage. There are a variety of methods to evaluate building seismic vulnerabilities with different computational cost and accuracy. In the meantime, fragility curves, which defines the possibility of structural damage as a function of ground motion characteristics and design parameters, are more common. These curves express the percentage of probability that the structural response will exceed the allowable performance limit at different seismic intensities. This study aims to obtain the fragility curve for low- and mid-rise structures of reinforced concrete moment frames by incremental dynamic analysis (IDA). These frames were exposed to an ensemble of 18 ground motions (nine records near-faults and nine records far-faults). Finally, after the analysis, their fragility curves are obtained using the limit states provided by HAZUS-MH 2.1. The result shows the near-fault earthquakes can drastically influence the fragility curves of the 6-story building while it has a minimal impact on those of the 3-story building.

• 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.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.132-132
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• 2015

Virtual metrology (VM) model based on plasma information (PI) parameter for C4F8 plasma-assisted oxide etching processes is developed to predict and monitor the process results such as an etching rate with improved performance. To apply fault detection and classification (FDC) or advanced process control (APC) models on to the real mass production lines efficiently, high performance VM model is certainly required and principal component regression (PCR) is preferred technique for VM modeling despite this method requires many number of data set to obtain statistically guaranteed accuracy. In this study, as an effective method to include the 'good information' representing parameter into the VM model, PI parameters are introduced and applied for the etch rate prediction. By the adoption of PI parameters of b-, q-factors and surface passivation parameters as PCs into the PCR based VM model, information about the reactions in the plasma volume, surface, and sheath regions can be efficiently included into the VM model; thus, the performance of VM is secured even for insufficient data set provided cases. For mass production data of 350 wafers, developed PI based VM (PI-VM) model was satisfied required prediction accuracy of industry in C4F8 plasma-assisted oxide etching process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.697-702
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• 2016

To control permanent magnet synchronous motors smoothly, it is important to know the exact parameter values of the stator resistance, various inductances, and the flux linkage of the permanent magnet. In practice, these parameters vary due to a variable operating point, temperature change, or a fault. This paper proposes a MRAS (Model Reference Adaptive System) based parameter estimator and adaptive control scheme. Owing to the non-linearity of the system equation with respect to these parameters, although many schemes proposed previously assumed that some parameters are known, all the parameters were assumed to be unknown. The simulation results revealed the effectiveness of the proposed algorithm.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.372-381
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• 2006

The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.112-117
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• 2013

The $\overline{x}$ control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the ($\overline{x}$, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the ($\overline{x}$, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.6
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• pp.52-59
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• 2010

As electric power demand of customers is constantly increasing, more bulk power systems are needed to install in a network. By development of renewable energies and high-efficient facilities and deregulated electricity market, moreover, the amount of distributed resource is considerably increasing in distribution network consequently. Also, distribution network has become more and more complex as mesh network to improve the distribution system reliability and increase the flexibility and agility of network operation. These changes make fault current increase. Therefore, the fault current will exceed a circuit breaker capacity. In order to solve this problem, replacing breaker, changing operation mode of system and rectifying transformer parameters can be taken into account. The SFCL(Superconducting Fault Current Limiter) is one of the most promising power apparatus. This paper proposes a methodology for on optimal location of SFCL. This place is defined as considering the decrement of fault current by component type and the increment of reliability by customer type according to an location of SFCL in a distribution network connected with DG(Distributed Generation). With case studies on method of determining optimal location for SFCL applied to a radial network and a mesh network respectively, we proved that the proposed method is feasible.

• Geophysics and Geophysical Exploration
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• v.11 no.2
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• pp.137-147
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• 2008

Fault rupture directivity of the Odaesan earthquake, which was inferred to be the main cause of the high PGAvalue (> 0.1 g) unusually observed at the near-source region, was analyzed by using the data from the nearby (R < 100 km) dense seismic stations. The Boatwright's method (2007) was adopted for this purpose in which the azimuth and takeoff angle of the unilateral rupture directivity function could be estimated based on the relative peak ground-motions of seismic stations resulting from the nature of the rupture directivity. In this study, the approximate values of the relative peak ground-motions was derived from the difference between the log residuals of the point-source spectral model (Boore, 2003) for the main and secondary events based on the Random Vibration Theory. In this derivation, the spectral difference for a frequency range between the source corner frequencies of main and secondary events was considered to reflect only the effect of the fault directivity. The inversion result of the model parameters for the fault directivity function showed that the fault-plane of NWW-SEE direction dipping steeply to the North with high rupture velocity near upward in SE direction is responsible for the observed high level of ground-motion at the near-source region.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.852-855
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• 1997

In this paper, the vibration diagnosis system of a rotating machinery is introduced, in which the vibration signals of multiple accelerometers and displacement sensors are used combinedly as input parameters and their characteristics of the vibration response and mutual relationships between each sensor signal are considered to improve the reliability of the diagnosis system. The fuzzy logic is utilized for inferencing the fault from the vibration signal patterns.