• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.816-821
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• 2013

This case study presents a practical method to reduce resonant vibration of generator-motor set. The generator is driven at 1800rpm by induction integral motor. The vibration is below alarm limits, but the dominant frequency of vibration is sub-synchronous and close to that of bearing fault signal in spite of new bearings. To find an mechanism of abnormal vibration, ODS and Modal analysis is carried out. The measured modal characteristics were compared with those of FE Analysis. The ODS mode at rated speed is consistent with system natural frequency mode that is excited by bearings. To reduce vibration level, the isolation mount at system base is changed with new rubber mount for lower natural frequency.

• Transactions on Electrical and Electronic Materials
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• v.7 no.6
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• pp.324-329
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• 2006

As the average usage period of transformers increases, it is becoming increasingly necessary to know the internal condition of transformers. It is therefore critically important to establish monitoring and diagnostic techniques that can perform transformer condition assessment. Frequency response analysis, generally known as FRA, is one of the technologies to diagnose transformers. Using case studies, this paper presents the effectiveness of FRA as measurements for detecting transformer failures. This paper introduces the fact that FRA waveforms have useful information about diagnosis of failure on core earths and winding shield, and that the condition outside transformers can affect frequency response characteristics.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.265-267
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• 2003

The wavelet transform(WT) has been extensively applied in solving many problems in applied science and engineering following its introduction in early 1980's. The WT analyzes a signal in a changeable frequency range by employing a moving window whereby along time window is used to obtain low frequency information and short time window is used to obtain high frequency information. In this paper, after various fault types in 154 KV transmission system was simulated by using EMTP, and the RMS values by changing Mother wavelet was calculated by applying wavelet transform to the simulated voltage and current signal.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.2
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• pp.104-112
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• 2015

This paper deals with an methodology for acquiring optimal order of rational function model in FDNE(frequency dependent network equivalents) with GA(genetic Algorithm). In order to analyze the modern power system with huge complexity, an practical and efficient equivalent model is needed which represents the system's characteristics of transient phenomenon. this paper shows developing a z domain rational function model which have the resultant coefficient from proposed GA simulation. To demonstrate this methodology, some simulations are performed with practical power system of NZ which applied with fault condition and nonlinear converter load.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.82-90
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• 1995

This paper pressents a condition-based maintenance (CBM) method through bibration analysis. The well known frequency analysis is employed for performing machine fault diagnosis. The statistical control chart is also applied for analyzing the trend of the bearing wear. Vibration sensors are attached to prototype machine and signals are continuously monitored. The sampled data are utilized to evaluate how well the fast fourier transform(FFT) and the statistical control chart techniques could be used to identify defects of machine and to analyze the machine degradation. Experimental results show that the propowed approach could classify every mal-function and could be utilized for real machine diagnosis system.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.81-86
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• 1997

This study is to estimate the possibility of accident in chemical plants from the analysis of system component which affects the occurrence of top event. Among the various risk assessment techniques, the Fault Tree Analysis which approaches deductively on the route of accident development was used in this study. By gate-by-gate method and minimal cut set, the qualitative and quantitative risk assessment for hazards in plants was performed. The probability of occurrence and frequency of top event was calculated from failure or reliability data of system components at stage of the quantitative risk assessment. In conclusion, the probability of accident was estimated according to logic pattern based on the Fault Tree Analysis. And the failure path which mostly influences on the occurrence of top event was found from Importance Analysis.

• Structural Engineering and Mechanics
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• v.23 no.4
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• pp.409-430
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• 2006

The aim of this study is to investigate the reliability of strong motion records processed by causal and acausal Butterworth filters in comparison to the results obtained from a synthetic accelerogram. For this purpose, the fault parallel component of the Bolu record of the Duzce earthquake is modeled with a sum of exponentially damped sinusoidal components. Noise-free velocities and displacements are then obtained by analytically integrating the synthetic acceleration model. The analytical velocity and displacement signals are used as a standard with which to judge the validity of the signals obtained by filtering with causal and acausal filters and numerically integrating the acceleration model. The results show that the acausal filters are clearly preferable to the causal filters due to the fact that the response spectra obtained from the acausal filters match the spectra obtained from the simulated accelerogram better than that obtained by causal filters. The response spectra are independent from the order of the filters and from the method of integration (whether analytical integration after a spline fit to the synthetic accelerogram or the trapezoidal rule). The response spectra are sensitive to the chosen corner frequency of both the causal and the acausal filters and also to the inclusion of the pads. Accurate prediction of the static residual displacement (SRD) is very important for structures traversing faults in the near-fault regions. The greatest adverse effect of the high pass filters is their removal of the SRD. However, the noise-free displacements obtained by double integrating the synthetic accelerogram analytically preserve the SRD. It is thus apparent that conventional high pass filters should not be used for processing near-fault strong-motion records although they can be reliably used for far-fault records if applied acausally. The ground motion parameters such as ARIAS intensity, HUSID plots, Housner spectral intensity and the duration of strong-motion are found to be insensitive to the causality of filters.

• Journal of the Korean Society for Railway
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• v.16 no.1
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• pp.26-31
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• 2013

The reduction unit is one of the most important components for railway vehicles because the torque of the motor must be transmitted to the wheels of the vehicle by the reduction unit. The faults in the reduction units of high-speed trains are caused by damage such as gear, fatigue. These have serious impacts on safety of the train during operation. To address this development of a system for monitoring, fault diagnosis of the reduction unit is needed to keep the vehicle running safely. Before that can be accomplished, it is most important to understand the vibration characteristics of the reduction unit in a normal state. Vibration diagnosis technology using characteristic-analysis of vibration waveform and frequency is known to be the most effective method for fault diagnosis. In this paper, we analyzed the vibration characteristics of the reduction units two Korean high-speed trains (KTX and KTX II), under normal conditions, by two test methods (driving gear test, full-vehicle test).