• Journal of the Society of Naval Architects of Korea
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• v.60 no.6
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• pp.441-449
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• 2023

With the enforcement of environmental regulations by the International Maritime Organization, the market for eco-friendly ships is expanding, and ships using electric propulsion devices are emerging as a promising solution. Many studies have been conducted to predict the failure of ships, but most of them are mainly research on the main diesel engine of ships. As the ship's propulsion method changes, new data is needed to predict the failure of electric propulsion ships. In this paper aims to analyze the failure characteristics of the electric propulsion system in consideration of the difference in the type of failure between the internal diesel engine and the electric propulsion system. The ship's propulsion unit assumed a DC motor and a signal pattern for normal conditions and general failure modes, but the failure record of the electric propulsion device operated on the actual ship was not available, so it generated a failure signal for small electric motor equipment to identify the failure signal. Assuming unbalance, misalignment, and bearing failure, which are the primary failure modes of the ship's electric motor, a failure signal was generated using a "rotator vibration data generator," and the frequency band, size, and phase difference of the measured vibration signal were analyzed to analyze the characteristics of each failure condition. Finally, the characteristics of each failure condition were identified so that the signals according to the failure type could be classified.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.219-223
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• 2002

Voltage and current signals during impulse tests on transformer are treated as non-stationary signals. A new method incorporating signal-processing method such as Wavelets and courier transform is proposed for failure identification. It is now possible to distinguish failure during impulse tests. The method is experimentally validated on a transformer winding. The wavelet transforms enables the detection of the time of occurrence of switching or failure events. After establishing the time of occurrence, the original waveform is split into two or more sections. The wavelet transform has ability to analysis the failure signal on time domain as well as frequency domain. Therefore, the wavelet transform is superior than courier transform to analysis the failure signal. In this paper, the fact was proved by real data which was achieved.

• Journal of the Korea Society of Computer and Information
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• v.6 no.2
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• pp.108-115
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• 2001

SDH-based SHR networks are the reconfiguration process in case of failure and APS protocol used. In this study, addresses the maximum allowed recovery time in two fiber bidirectional networks. We analyse the APS protocol and derive the Processing time domains of each n order to cope with the maximum reconfiguration time of 50㎳, as specified in the ITU-T standard. We finally analyze the interleaved failures. One is the signal degrade then the signal failure, the other is the signal failure then the signal failure. Any case analysis is carried out. reconfiguration time can be guaranteed.

• Journal of Navigation and Port Research
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• v.32 no.2
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• pp.133-140
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• 2008

In this paper, a fault detection method for operator failures using the observation technique is proposed. The suggested algorithm is extended using the conventional sensor/actuator fault detection method. First, it is assumed that operator failure affects human work operations, as it is an external input signal. With this assumption, a human work model with operator failure is suggested. Second, an unknown input observer with proportional and integral gains is introduced. The characteristic of this observer of estimating an external signal without an exact input is shown, and the conditions for the detection of an operator failure are proposed. Finally, by simulating the container crane operations, it is verified that the observer can accurately detect an operator failure and estimate its magnitude from the given internal signal.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.743-748
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• 2010

The stability forecasting of rock slope is more difficult than soil slope because catching the sign of failure in monitoring is not easy and deformation of the rock is small in failure process. But in the rock slope, there is small deformation like crack propagation in rock itself and it accumulates gradually in failure process. If it is possible to detect the small change in the rock slope, we can know the failure time exactly. Because the individual signal is gathered in the acoustic emission monitoring, it is possible to monitoring the slope if many sound signal is accumulated. Detection test of acoustic emission was performed. Uniaxial, two types of bending test, and two plane shear test were done with various cement paste sample. Wave propagation velocity of uniaxial test sample was increased with curing time. Wave Analysis give us the result that there is a AE sign signal before the failure, the AE count is suddenly increased. And frequency level 125kHz before failure is changed to level 200-250kHz after failure. In two plane shear test we can catch the AE signal and can know the failure type from wave shape. Monitoring test site is tunnel slope in Hongcheon but special signal is not collected.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.789-794
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• 2001

The acoustic emission(AE) signal from concrete cylinder specimen during failure process under cycling compressive loads were recorded and analyzed. Different filters were set on the AE signal duration based on the characteristic of amplitude distribution. From the value of AE signal amplitude, which corresponds to the occurrence of the peak for AE hits, the AE signals from concrete compressive specimen were divided into five sections. The relationship between the AE signal section and the failure mechanism of these materials, analyzed on the meso-structure level was determined. Based on the experiments, the AE characteristics of each failure mechanism are given. The results show that the AE technique is a valuable tool to study the failure mechanism of concrete.

• Tribology and Lubricants
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• v.35 no.5
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• pp.317-322
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• 2019

In spite of progress in tribological research, machine component failure due to friction and wear has been reported frequently. This failure may lead to secondary damage that can cause huge expense for maintenance and repair. To prevent economic loss, it is important to detect and predict the initial failure point. In this sense, various researchers have been tried to develop Condition Monitoring (CM) method using Acoustic Emission (AE) generated while the materials undergo failure. In this study, effect of particles on friction and wear was investigated using the pin-on-plate friction test and AE signal was recorded with a band-width type AE sensor. The experiments were performed in dry and lubricant conditions using steel and glass as specimens. After the experiment, 3D laser microscope image was captured to evaluate the wear behavior quantitatively. The AE signal was analyzed in time-domain and frequency-domain. The amplitude was compared with the frictional results. The results of this study showed that particle generation accelerate wear, generate high magnitude AE signal and change the frequency characteristics of the signal. Also, lubricant condition test results showed low coefficient of friction, low wear rate, and low magnitude of AE signal compared to the dry condition. It is expected that the results of this study will aid in better assessment of wear in CM technology

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1109-1110
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• 2008

It is required to optimize the system operation efficiency to allocate maintenance task and period using systemic maintenance process. To allocate maintenance task and period must analysis the failure distribution mode at first. In this paper, we introduce the linear regression analysis and estimate the failure distribution for the railroad signal equipment using that.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.175-177
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• 1995

The sensor signal validation and failure detection system must be able to detect, isolate, and identify sensor degradation as well as provide a reconstruction of the measurements. In this study, this is accomplished by combining the neural network, the Generalized Consistency Checking(GCC), and the Sequential Probability Ratio Test(SPRT) method in a decision estimator module. The GCC method is a computationally efficient system for redundant sensors, while the SPRT provides the ability to make decisions based on the degradation history of a sensor. The methodology is also extended to the detection of noise degradation. The acceptability of the proposed method is demonstration by using the simulation data in safety injection accident of nuclear power plants. The results show that the signal validation and sensor failure detection system is able to detect and isolate a bias failure and noise type failures under transient conditions. And also, the system is able to provide the validated signal by reconstructing the measurement signals in the failure conditions considered.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2000.11a
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• pp.128-133
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• 2000

The objective of this research is to develop real-time failure detection techniques for damage assessment of composite materials using optical fiber sensors. Signals from matrix cracking or fiber fracture in composite laminates are treated by signal processing unit in real-time. This paper describes the implementation of time-frequency analysis such as the Short Time Fourier Transform(STFT) to determine the time of occurrence of failure. In order to verify the performance of the optical fiber sensor for stress wave detection, we performed pencil break test with EFPI sensor and compared it with that of PZT. The EFPI sensor was embedded in composite beam to sense the failure signals and a tensile test was performed. The signals of the fiber optic sensor when damage occurred were characterized using STFT and wavelet transform. Failure detection system detected the moment of failure accurately and showed good sensitivity with the infinitesimal failure signal.