• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.2
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• pp.96-103
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• 2005

The early detection of the bearing defects in rotating machinery is very important since the critical failure of bearing causes a machinery shutdown. However it is not easy to detect the vibration signal caused by the initial defects of bearing because of the high level of random noise. A signal processing technique, called the adaptive line enhancer(ALE) as one of adaptive filter, is used in this study. This technique is to eliminate random noise with little a prior knowledge of the noise and signal characteristics. Also we propose the optimal methods fir selecting the three main ALE parameters such as correlation length filter order and adaptation constant. Vibration signals f3r three abnormal bearings, including inner and outer raceways and ball defects, were acquired by Anderon(angular derivative of radius on) meter. The experimental results showed that ALE is very useful f3r detecting the bearing defective signals masked by random noise.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.527-532
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• 2014

Generator is the main equipment of a power plant that generates electric power. The line frequency is 60Hz, since that is operated at 3600rpm in fossil power plant. Therefore, the specific frequency 120Hz by the electromagnetic excitation force is generated inherently. If the looseness of stator at generator happens, abnormal sound that has 120Hz and the harmonic frequency is emitted from generator frame. In that case, binding of end-winding or re-wedging is needed for the reduction of sound level. In case of severe level of sound, belly band can be additionally installed at core. In this paper, the characteristics of generator sound is described and modal data is analyzed after installation of belly band.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.111-121
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• 2011

Intelligent video analysis systems require techniques which can predict accidents and provide alarms to the monitoring personnel. In this paper, we present an abnormal behavior analysis technique based on adaptive background generation. More specifically, abnormal behaviors include fence climbing, abandoned objects, fainting persons, and loitering persons. The proposed video analysis system consists of (i) background generation and (ii) abnormal behavior analysis modules. For robust background generation, the proposed system updates static regions by detecting motion changes at each frame. In addition, noise and shadow removal steps are also were added to improve the accuracy of the object detection. The abnormal behavior analysis module extracts object information, such as centroid, silhouette, size, and trajectory. As the result of the behavior analysis function objects' behavior is configured and analyzed based on the a priori specified scenarios, such as fence climbing, abandoning objects, fainting, and loitering. In the experimental results, the proposed system was able to detect the moving object and analyze the abnormal behavior in complex environments.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.6
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• pp.655-662
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• 2012

Gas leakage in an oil refinery causes damage to the environment and unsafe conditions. Therefore, it is necessary to develop a technique that is able to detect the location of the leakage and to filter abnormal gas-leakage signals from normal background noise. In this study, the adaptation filter of the finite impulse response (FIR) least mean squares (LMS) algorithm and a cross-correlation function were used to develop a leakage-predicting program based on LABVIEW. Nitrogen gas at a high pressure of 120 kg/$cm^2$ and the assembled equipment were used to perform experiments in a reverberant chamber. Analysis of the data from the experiments performed with various hole sizes, pressures, distances, and frequencies indicated that the background noise occurred primarily at less than 1 kHz and that the leakage signal appeared in a high-frequency region of around 16 kHz. Measurement of the noise sources in an actual oil refinery revealed that the noise frequencies of pumps and compressors, which are two typical background noise sources in a petrochemical plant, were 2 kHz and 4.5 kHz, respectively. The fact that these two signals were separated clearly made it possible to distinguish leakage signals from background noises and, in addition, to detect the location of the leakage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.672-678
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• 2020

Ultra-high-speed images that accurately depict the minute movements of objects have become common as low-cost and high-performance cameras that can film at high speeds have emerged. In this paper, the proposed method removes unexpected noise contained in images after input at high speed, and then extracts an area of interest that can represent personal information, such as skin areas, from the image in which noise has been removed. In this paper, noise generated by abnormal electrical signals is removed by applying bilateral filters. A color model created through pre-learning is then used to extract the area of interest that represents the personal information contained within the image. Experimental results show that the introduced algorithms remove noise from high-speed images and then extract the area of interest robustly. The approach presented in this paper is expected to be useful in various applications related to computer vision, such as image preprocessing, noise elimination, tracking and monitoring of target areas, etc.

• Journal of Biomedical Engineering Research
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• v.5 no.2
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• pp.173-182
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• 1984

This paper represents a algorithm which improves the some drawbacks in the past methods for detecting QRS Complex waves. This proposed algorithm is very useful to detect correctly QRS Complex not only in a normal ECG, but in the abnormal ECG such as contaminating the noise with high amplitude, the existence of sharp T wave, and abrupt stepwise fluctuation of the base line.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.3
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• pp.198-206
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• 2011

This paper presents a new programmable compensation circuit (PCC) for a System-on-Chip (SoC). The PCC is integrated with $0.18-{\mu}m$ BiCMOS SiGe technology. It consists of RF Design-for-Testability (DFT) circuit, Resistor Array Bank (RAB) and digital signal processor (DSP). To verify performance of the PCC we built a 5-GHz low noise amplifier (LNA) with an on-chip RAB using the same technology. Proposed circuit helps it to provide DC output voltages, hence, making the RF system chain automatic. It automatically adjusts performance of an LNA with the processor in the SoC when it goes out of the normal range of operation. The PCC also compensates abnormal operation due to the unusual PVT (Process, Voltage and Thermal) variations in RF circuits.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.284-285
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• 2008

For development of low fuel consumption vehicle, design of engine can he changed and it brings collateral effect. Abnormal vibration is one of bad effect of high efficiency engine and it can be got over by active engine mount system. For development of active engine mount system, an adequate experimental equipment is required which contain characteristic of real engine movement. To make adequate experimental equipment, regulated force vibrating system including characteristics of vehicle chassis is required.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1382-1385
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• 2007

In this paper, we study about the measuring algorithm that can implement Structural Health Monitoring (SHM) more efficiently by two measurement methods using smart sensor. Through the impedance measurement method, the damage condition of structures on wide area is monitored first, and then it changes the mode to guided wave measurement mode by mode switching algorithm when impedance measurement mode detects abnormal signals. Efficient handling of the real-time data would be available by analyzing location and shape of damage through guided wave measurement.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1386-1390
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• 2007

This paper presents the equipment condition monitoring technology for the process or the equipment using statistical techniques. The equipment condition monitoring system consists of an empirical model to estimate the expected sensor values of process variables and a diagnose model to detect the abnormal condition and to identify the root source of the problem. The empirical model is constructed by the analysis of historic data. The diagnose model uses the sequential probability ratio test (SPRT) technique. The monitoring system was tested with real operating data acquired from the Reactor Coolant Pump Seal in the Nuclear Power Plant. It can detect the system degradation or failure at the early stage since it is able to catch the subtle deviation of process variables from normal condition.