• Journal of Biosystems Engineering
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• 제32권3호
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• pp.190-196
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• 2007

The purpose of this study is to develop a measurement system of cutter conditions for combine header diagnosis during rice harvesting. A load cell was installed at the locker-arm to measure load fluctuation and an acceleration senor was used to monitor vibration signal of cutter bar. The data were collected from a paddy field during harvesting. The tests were conducted with a normal cutter, a loosened cutter, a broken cutter, and a worn-out connecter pin at the field. The vibration signals converted by FFT (Fast Fourier Transformation), filtered, and normalized. The load data and peak values of vibration signals in four different frequency ranges were used to determine the cutting operation and the cutter conditions of combine. The multiple comparison tests showed that the load data and peak values of vibration signals were important to monitor the cutting operation and cutter conditions of combine header.

• Structural Engineering and Mechanics
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• 제46권1호
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• pp.75-90
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• 2013

In order to identify damage of highway bridges rapidly, a method for damage identification using dynamic response of bridge induced by moving vehicle and static test data is proposed. To locate damage of the structure, displacement energy damage index defined from the energy of the displacement response time history is adopted as the indicator. The displacement response time histories of bridge structure are obtained from simulation of vehicle-bridge coupled vibration analysis. The vehicle model is considered as a four-degree-of-freedom system, and the vibration equations of the vehicle model are deduced based on the D'Alembert principle. Finite element method is used to discretize bridge and finite element model is set up. According to the condition of displacement and force compatibility between vehicle and bridge, the vibration equations of the vehicle and bridge models are coupled. A Newmark-${\beta}$ algorithm based professional procedure VBAP is developed in MATLAB, and used to analyze the vehicle-bridge system coupled vibration. After damage is located by employing the displacement energy damage index, the damage extent is estimated through the least-square-method based model updating using static test data. At last, taking one simply supported bridge as an illustrative example, some damage scenarios are identified using the proposed damage identification methodology. The results indicate that the proposed method is efficient for damage localization and damage extent estimation.

• 터널과지하공간
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• 제17권6호
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• pp.484-492
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• 2007

국내에서는 주로 환산거리 진동예측식에 의한 발파진동 예측 방법이 사용되고 있다. 그러나 이러한 환산거리방식은 실규모의 발파가 시행되어져야 할 필요성이 있다. 최근 국내에서는 터널 등의 공사 시행 전 사전 조사단계에서 발파진동의 영향권을 예측하려는 시도로서 지질 조사용 시추공 등에 장약 발파하여 지반진동을 측정하고 본 발파의 발파 진동을 예측하는 방법이 사용되고 있다. 그러나 이러한 발파진동 예측 방법은 본 발파시의 진동의 전달 특성을 완전하게 반영하지는 못한다. 이러한 발파진동 예측방법의 결점을 보완하기 위하여 본 연구에서는 사전 조사 단계의 단일공 파형 중첩 모델링을 통하여 발파진동을 예측하는 방법을 개발하였다.

• 농업과학연구
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• 제17권2호
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• pp.115-129
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• 1990

This study was carried out to develop a microcomputer-based data acquisition system and software for analysis of engine vibration. Due to this development of system and software, studies on the vibration of engine was able to conduct efficiently. From the display of CRT, the outputs of test was compared and analysed on a zero-base motoring. The results obtained are summarized as follows : 1. Data acquisition system in microcomputer were designed and programmed and operated accurately. 2. The result of test was easily compared in monitoring of zero-base at color monitor. 3. This system and program developed was able to control the measuring intervals, no. of channels, and no. of data as wated. 4. The type of vibration of engine was type of bi-mode from the FFT and statistical analysis. 5. The maxima of engine vibration were 9.11 G at X-axis. 11.27 G at Y-axis, and 4.9 G at Z axis for full load of net output 6. The maxima of engine vibration unloaded were 4.28 G, 5.59 G, 2.68 G at 2200 rpm.

• 한국소음진동공학회논문집
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• 제15권2호
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• pp.192-198
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• 2005

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The results by EMD method whichhas used only output vibration data are almost identical to the results by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

• 국제초고층학회논문집
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• 제1권4호
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• pp.283-300
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• 2012

Wind-induced vibrations of buildings can be perceived when movement of objects caused by the vibration is seen. However, movement of objects that would normally be expected to trigger visual perception of building vibrations is not necessarily seen in actual building environments. Therefore, to evaluate habitability to building vibrations, it is necessary to examine the influence of movement of objects on vibration perception taking into account probability of seeing the objects. As the first step in this study, those data necessary to estimate probability of vibration perception from seeing of swaying objects have been measured during normal activities in actual buildings. In addition, statistical analysis of the data has also been carried out. As the second step in this study, the probability distribution of vibration perception by visual sensation is estimated using the series of data measured in the first step. Probability of seeing object is considered in the estimated probability distribution.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.699-704
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• 2004

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The result by EMD method which has used only output vibration data is almost identical to the result by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

• 한국소음진동공학회논문집
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• 제12권10호
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• pp.799-807
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• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• Structural Engineering and Mechanics
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• 제60권4호
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• pp.655-665
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• 2016

This paper aims to develop models to accurately predict the behavior of fresh concrete exposed to vibration using artificial neural networks (ANNs) model and regression model (RM). For this purpose, behavior of a full scale precast concrete mold was investigated experimentally and numerically. Experiment was performed under vibration with the use of a computer-based data acquisition system. Transducers were used to measure time-dependent lateral displacements at some points on mold while both mold is empty and full of fresh concrete. Modeling of empty and full mold was made using both ANNs and RM. For the modeling of ANNs: Experimental data were divided randomly into two parts. One of them was used for training of the ANNs and the remaining part was used for testing the ANNs. For the modeling of RM: Sinusoidal regression model equation was determined and the predicted data was compared with measured data. Finally, both models were compared with each other. The comparisons of both models show that the measured and testing results are compatible. Regression analysis is a traditional method that can be used for modeling with simple methods. However, this study also showed that ANN modeling can be used as an alternative method for behavior of fresh concrete exposed to vibration in precast concrete structures.

• Smart Structures and Systems
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• 제17권3호
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• pp.471-489
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• 2016

Modal identification based on ambient vibration data has attracted extensive attention in the past few decades. Since the excitation for ambient vibration tests is mainly from the environmental effects such as wind and traffic loading and no artificial excitation is applied, the signal to noise (s/n) ratio of the data acquired plays an important role in mode identifiability. Under ambient vibration conditions, certain modes may not be identifiable due to a low s/n ratio. This paper presents a study on the mode identifiability of an instrumented cable-stayed bridge with the use of acceleration response data measured by a long-term structural health monitoring system. A recently developed fast Bayesian FFT method is utilized to perform output-only modal identification. In addition to identifying the most probable values (MPVs) of modal parameters, the associated posterior uncertainties can be obtained by this method. Likewise, the power spectral density of modal force can be identified, and thus it is possible to obtain the modal s/n ratio. This provides an efficient way to investigate the mode identifiability. Three groups of data are utilized in this study: the first one is 10 data sets including six collected under normal wind conditions and four collected during typhoons; the second one is three data sets with wind speeds of about 7.5 m/s; and the third one is some blind data. The first two groups of data are used to perform ambient modal identification and help to estimate a critical value of the s/n ratio above which the deficient mode is identifiable, while the third group of data is used to perform verification. A couple of fundamental modes are identified, including the ones in the vertical and transverse directions respectively and coupled in both directions. The uncertainty and s/n ratio of the deficient mode are investigated and discussed. A critical value of the modal s/n ratio is suggested to evaluate the mode identifiability of the deficient mode. The work presented in this paper could provide a base for the vibration-based condition assessment in future.