• Structural Monitoring and Maintenance
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• v.3 no.2
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• pp.115-127
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• 2016

It is a challenging problem of assessing the location and extent of structural damages with vibration measurements. In this paper, an improved Extended Kalman filter (EKF) with Tikhonov regularization is proposed to identify structural damages. The state vector of EKF consists of the initial values of modal coordinates and damage parameters of structural elements, therefore the recursive formulas of EKF are simplified and modal truncation technique can be used to reduce the dimension of the state vector. Then Tikhonov regularization is introduced into EKF to restrain the effect of the measurement noise for improving the solution of ill-posed inverse problems. Numerical simulations of a seven-story shear-beam structure and a simply-supported beam show that the proposed method has good robustness and can identify the single or multiple damages accurately with the unknown initial structural state.

• Structural Monitoring and Maintenance
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• v.3 no.2
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• pp.175-194
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• 2016

Based on the change of traditional viscoelastic damper structure, a brand-new damper is designed to control simultaneously the translational vibration and the rotational vibration for platforms. Experimental study has been carried out on the mechanical properties of viscoelastic material and on its multi-dimensional seismic response control effect of viscoelastic damper. Three types of viscoelastic dampers with different shapes of viscoelastic material are designed to test the influence of excited frequency, strain amplitude and ambient temperature on the mechanical property parameters such as circular dissipation per unit, equivalent stiffness, loss factor and storage shear modulus. Then, shaking table tests are done on a group of single-storey platform systems containing one symmetric platform and three asymmetric platforms with different eccentric forms. Experimental results show that the simulation precision of the restoring force model is rather good for the shear deformation of viscoelastic damper and is also satisfied for the torsion deformation and combined deformations of viscoelastic damper. The shaking table tests have verified that the new-type viscoelastic damper is capable of mitigating the multi-dimensional seismic response of offshore platform.

• Smart Structures and Systems
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• v.22 no.2
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.83-91
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• 1997

A reactor vibration monitoring has been performed using neutron noise obtained from excore detectors for the safety operation, Traditionally, the spectral estimator based on Fourier analysis has been widely used in the noise analysis of the reactor system. If the bias is too severe, the resolution would not be adequate for a given application. One major motivation for the current interests in the parametric approach to spectral estimation is the apparent higher resolution achievable with these modern techniques. In considering an unbias, a consistency, an efficency, and a minimum lower bound of the statictic estimation, an AR model is appropriate for noise spectral estimation with sharp peaks but not deep valley. In order to select an appropriate model order, the lag value of autocorrleaton function is applied. Burg method to trace the vibration mode of RPV internal is the most sucuessful.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.581-586
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• 2000

The equivalent source method is used to calculate the internal pressure field for an enclosure which can have arbitrary boundary conditions and may include internal objects which scatter the sound. Some of the equivalent positions are chosen to be the same as the first order images of the source inside the enclosure, some are positioned on a spherical surface some distance outside the enclosure. The normal velocity on the surfaces of the enclosure walls is evaluated at a larger number of positions than there are equivalent sources. The sum of the squared difference between this velocity and the expected is minimized by adjusting the strength of the equivalent sources. The convergence of this method is checked by evaluating the velocity error at a larger number of monitoring positions. Example results are presented for various numbers of sources and evaluation points. The results showed that in general the more equivalent sources increased the accuracy of the sound field predictions but the accuracy is not too much sensitive to the numbers of evaluation points.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.2
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.739-744
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• 2013

The aim of this study is to evaluate the noise level from the machines used for tunnel construction and to analyze the noise exposure level of workers engaged in tunneling works. The sound level meter and noise dosimeters was used for the monitoring of noise in the tunneling work sites. The average noise from jumbo drill was 113.0 dE(A), the noise from pay loader was 92.4 dB(A), the noise from backhoe was 99.9 dB(A) and the noise from shotcrete machine was 94.3 dE(A). The tunneling workers were exposed to 66.9~94.9 dB(A) of noise and other workers exposed to less than 90 dB(A) of noise. Jumbo drill operators were exposed to to 82.5~84.2 dB(A) of noise, backhoe operators were exposed to 70.2~94.9 dB(A) of noise, shotcrete machine operators were exposed to 68.2~74.7 dB(A) of noise and pay loader operators were exposed to 59.2~81.3 dE(A) of noise.

• Proceedings of the KSEE Conference
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• 2006.10a
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• pp.153-166
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• 2006

종래의 발파진동의 분석은 주로 대상 지장물에 대한 피해한계를 정립시키고자 하는 관점에서 발파공에서 비교적 원거리의 진동특성을 이해하기 위해 수행되어져 왔으나, 최근의 추이는 발파공 주변 암반의 손상의 정도를 평가하고자 하는 관점에서 그 분석영역의 범위가 근거리 진동특성 연구분야로 확대되고 있는 실정이다. 특히, 터널 발파작업시 여굴의 발생, 암반사면의 안정성 검토 등의 목적으로 암반의 손상영역을 평가하는데 있어서, 기초자료로 활용하고자 많은 연구가 이루어지고 있다. 암반손상의 평가를 위한 손상권 예측방법에는 여러 가지가 있으나, 그 중에서 대부분이 발파진동속도에 근거하고 있으며, 평가를 위한 진동의 예측은 기존에는 원거리 진동특성을 이용하여 근거리 진동을 예측하는 방법으로 그 손상의 정도를 평가하였으나, 최근의 추세는 계측기의 발달로 수m 이내의 진동특성의 계측이 가능하게 되었다. 이와 관련하여 국외에서는 수차례의 실험결과가 여러 문헌에서 보고되고 있으나, 실험장비의 선택 및 측정방법의 어려움 등의 연유로 국내에서는 아직까지 실시되지 못하고 있는 상황이다. 따라서, 본 연구에서는 실제 발파공 근접진동(near field vibration) 계측을 실시하고 그 결과를 분석하여 추후 지속적인 근거리 진동측정 방법 및 평가방법에 대한 기초자료를 제공하고자 하였으며, 무엇보다도 어떻게 계측할 것인가 하는 계측방법 및 그 계측결과의 분석방법에 대해 문제점 파악 및 향후 보완점에 대해 비중을 두어 수행하였다.

• Structural Engineering and Mechanics
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• v.15 no.1
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• pp.115-134
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• 2003

The Ting Kau Bridge in Hong Kong is a cable-stayed bridge comprising two main spans and two side spans. The bridge deck is supported by three towers, an end pier and an abutment. Each of the three towers consists of a single reinforced concrete mast which reduces its section in steps, and it is strengthened by transverse cables and struts in the transverse vertical plane. The bridge deck is supported by four inclined planes of cables emanating from anchorages at the tower tops. In view of the threat from typhoons, the dynamic behaviour of long-span cable-supported bridges in the region is always an important consideration in their design. This paper is devoted to the ambient vibration measurements of the bridge for evaluation of dynamic characteristics including the natural frequencies and mode shapes. It also describes the modelling of the bridge. A few finite element models are developed and calibrated to match with the field data and the results of subsequent structural health monitoring of the bridge.

• Smart Structures and Systems
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• v.15 no.3
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• pp.699-715
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• 2015

A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) "Is there a change in the current state of the structure compared to the baseline state?", (b) "Does the change indicate a localized stiffness reduction or increase?", with the latter representing a situation of retrofitting operations, and (c) "What is the severity of the change in a probabilistic sense?". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple "non-damage" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.