• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.111-121
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• 2018

Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

• BMB Reports
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• v.51 no.5
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• pp.225-229
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• 2018

In humans, hormonal regulation is crucial for the preparation of uterine environment leading to either successful implantation or menstrual cycle. Estrogen is a pivotal female steroid hormone that regulates the uterine dynamics along with progesterone in the estrous and menstrual cycles in humans. Estrogen signals act via nuclear estrogen receptor or membrane-bound receptor. The membrane-bound estrogen receptor plays a crucial role in the rapid response of estrogen in the uterine epithelium. Recently, RASD1 has received attention as a novel signal transducer of estrogen in various systems including female reproductive organs. In this review, we discuss the regulation of estrogen and RASD1 signaling in the uterus and also provide insights into RAS as a novel signaling molecule in repeated implantation failure.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.343-346
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• 2008

To improve the reliability and extend the life for a wind-turbine gearbox, the gearbox needs to be monitored and analysed exactly. This study was conducted to analyze and detect the gearbox conditions when lubricating oil contaminated by wear particles was used. Characteristics of the gearbox failure by wear particles were monitored simultaneously by the on-line measurement sensor of vibration, oil condition and temperature. For the detail vibration analyses, frequency analysis(FFT) was performed. The results of the study were summarized as follows: Vibrational signal was found sensitive to abnormal changes of the gearbox conditions when lubricant was contaminated by wear particles. Also, using frequency analysis for the harmonics of gear mesh frequency(GMF), it is found that the failure of gearbox was caused by the damages of meshing gears. However, temperature and oil condition measuring signals were found not so effective to detect any gearbox failure by oil contamination.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.5
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• pp.1-6
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• 2010

The wavelet transform is a popular tool for studying intermittent and localized phenomena in signals. In this study the wavelet transform of cutting force signals was conducted for the detection of a tool failure in turning process. We used the Daubechies wavelet analyzing function to detect a sudden change in cutting signal level. A preliminary stepped workpiece which had intentionally a hard condition was cut by the inserted cermet tool and a tool dynamometer obtained cutting force signals. From the results of the wavelet transform, the obtained signals were divided into approximation terms and detailed terms. At tool failure, the approximation signals were suddenly increased and the detailed signals were extremely oscillated just before tool failure.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.1
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• pp.66-71
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• 2004

Airborne contaminant particles are intruded into optical disk drive(ODD) due to the flow caused by disk rotation and can be adhered to objective lens, which causes read/write errors. Such a phenomenon can be a serious problem for high-density storage devices. The purpose of this paper is to understand the effect of particle contamination of objective lens in a CD-ROM drive on laser diode power and photo diode RF signal. The measurements of laser power and readout RF signal were carried out by using a laser power meter and a time interval analyzer, respectively. The parameters for estimating a readout-signal' distortion were its jitter and amplitude. Alumina(Al$_2$O$_3$) particles were used as test dust particles. The results show that the failure for data access happened as the degree of lens contamination was greater than 20％.

• Journal of Applied Biological Chemistry
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• v.53 no.2
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• pp.105-107
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• 2010

To overcome the limitation of E. coli expression system such as inclusion body formation and disulfide bond failure, we tried to express the heterologous protein as a secreted form. We adopted agarase signal peptide (ASP; 23 amino acid residues) from Agarivorans albus YKW-34 which is one of marine bacteia. When we used ASP to express $\beta$-agarase, about 42% activity was detected in media.

• Proceedings of the KIEE Conference
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• 1992.07b
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• pp.934-937
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• 1992

The partial discharge signal can be used as the detection signal of the deterioration of the power transformer insulation. Detecting this signal, the insulation failure and the point, in which the partial discharge is generated, could be predicted. In this paper, we study to predict the point, and find that the cross correlation method could predict the point accurately. Also, experimental system and the results are presented here.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.905-908
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• 2007

The purpose of this work is to obtain fundamental data about fatigue crack detection of the welded exhaust flange by using the AE method. The acoustic emission method as a nondestructive evaluation is one of high technical test for realtime monitoring in the dangerous industry fields. Signal analysis of both AE sensor and accelerometer for fatigue crack failure are presented in this paper.

• Wind and Structures
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• v.20 no.3
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• pp.405-422
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• 2015

In previous model- and full-scale studies, high-amplitude vertical vibrations of mast-arm traffic signal structures have been shown to be due to vortex shedding, a phenomenon in which alternatingly shed, low-pressure vortices induce oscillating forces onto the mast-arm causing a cross-wind response. When the frequency of vortices being shed from the mast-arm corresponds to the natural frequency of the structure, a resonant condition is created causing long-lasting, high-amplitude vibrations which may lead to the fatigue failure of these structures. Turbulence in the approach flow is known to affect the cohesiveness of vortex shedding. Results from this full-scale investigation indicate that the surrounding terrain conditions, which affect the turbulence intensity of the wind, greatly influence the likelihood of occurrence of long-lasting, high-amplitude vibrations and also impact whether reduced service life due to fatigue is likely to be of concern.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.765-771
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• 1998

An artificial neural network(NN) technique is developed for hardware redundant sensor validation. Since the measurement space is a continuous space with many operating regions, it is difficult to train a NN to correctly detect failure in an accurate measurement system. A conventional backpropagation NN is modified to include an additional preprocessing layer that extracts classification features from scalar measurements. This feature extraction means transform the measurement space to parity space. The NN is independent of the state variable being measured, the instrument range, and the signal tolerance. This NN resembles the parity space approach to signal validation, except that analytical parity equations are unneeded and the NN pattern recognition capability is utilized for decision making.