• Smart Structures and Systems
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• 제2권2호
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• pp.141-154
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• 2006

Recently, the Hilbert-Huang transform (HHT) has gained considerable attention as a novel technique of signal processing, which shows promise for the system identification and damage detection of structures. This study investigates the effectiveness and accuracy of the HHT method for the system identification and damage detection of structures through a series of experiments. A multi-degree-of-freedom (MDOF) structural model has been constructed with modular members, and the columns of the model can be replaced or removed to simulate damages at different locations with different severities. The measured response data of the structure due to an impulse loading is first decomposed into modal responses using the empirical mode decomposition (EMD) approach with a band-pass filter technique. Then, the Hilbert transform is subsequently applied to each modal response to obtain the instantaneous amplitude and phase angle time histories. A linear least-square fit procedure is used to identify the natural frequencies and damping ratios from the instantaneous amplitude and phase angle for each modal response. When the responses at all degrees of freedom are measured, the mode shape and the physical mass, damping and stiffness matrices of the structure can be determined. Based on a comparison of the stiffness of each story unit prior to and after the damage, the damage locations and severities can be identified. Experimental results demonstrate that the HHT method yields quite accurate results for engineering applications, providing a promising tool for structural health monitoring.

• 풍력에너지저널
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• 제9권4호
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• pp.16-23
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• 2018

A damage detection method for the tripod support structure of offshore wind turbines is presented for structural health monitoring. A finite element model of a prototype tripod support structure is established and the modal properties are calculated. The degree and location of the damage are estimated based on the neural network technique using the changes of natural frequencies and mode shape due to the damage. The stress distribution occurring in the support structure is obtained by a dynamic analysis for the wind turbine system to select the output data of the neural network. The natural frequencies and mode shapes for 36 possible damage scenarios were used for the input data of the learned neural network for damage assessment. The estimated damages agreed reasonably well with the accurate ones. The presented method could be effectively applied for damage detection and structural health monitoring of various types of support structures of offshore wind turbines.

• Steel and Composite Structures
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• 제19권1호
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• pp.191-208
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• 2015

The sensitivity-based finite element model updating method has received increasing attention in damage detection of structures based on measured modal parameters. Finding an optimization technique with high efficiency and fast convergence is one of the key issues for model updating-based damage detection. A new simple and computationally efficient optimization algorithm is proposed and applied to damage detection by using finite element model updating. The proposed method combines the Gauss-Newton method with region truncation of each iterative step, in which not only the constraints are introduced instead of penalty functions, but also the searching steps are restricted in a controlled region. The developed algorithm is illustrated by a numerically simulated 25-bar truss structure, and the results have been compared and verified with those obtained from the trust region method. In order to investigate the reliability of the proposed method in damage detection of structures, the influence of the uncertainties coming from measured modal parameters on the statistical characteristics of detection result is investigated by Monte-Carlo simulation, and the probability of damage detection is estimated using the probabilistic method.

• Smart Structures and Systems
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• 제23권1호
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• pp.45-60
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• 2019

This paper is devoted to proposing a new approach for damage detection of structures. In this technique, the biconjugate gradient method (BCG) is employed. To remedy the noise effects, a new preconditioning algorithm is applied. The proposed preconditioner matrix significantly reduces the condition number of the system. Moreover, based on the characteristics of the damage vector, a new direct search algorithm is employed to increase the efficiency of the suggested damage detection scheme by reducing the number of unknowns. To corroborate the high efficiency and capability of the presented strategy, it is applied for estimating the severity and location of damage in the well-known 31-member and 52-member trusses. For damage detection of these trusses, the time history responses are measured by a limited number of sensors. The results of numerical examples reveal high accuracy and robustness of the proposed method.

• Smart Structures and Systems
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• 제24권2호
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• pp.233-242
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• 2019

In this paper, an inverse approach based on uniform load surface (ULS) is presented for structural damage localization and quantification. The ULS is excellent approximation for deformed configuration of a structure under distributed unit force applied on all degrees of freedom. The ULS make use of natural frequencies and mode shapes of structure and in mathematical point of view is a weighted average of mode shapes. An objective function presented to damage detection is discrepancy between the ULS of monitored structure and numerical model of structure. Solving this objective function to find minimum value yields damage's parameters detection. The teaching-learning based optimization algorithm has been employed to solve inverse problem. The efficiency of present damage detection method is demonstrated through three numerical examples. By comparison between proposed objective function and another objective function which make use of natural frequencies and mode shapes, it is revealed present objective function have faster convergence and is more sensitive to damage. The method has good robustness against measurement noise and could detect damage by using the first few mode shapes. The results indicate that the proposed method is reliable technique to damage detection in structures.

• Smart Structures and Systems
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• 제16권6호
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

• 전기전자학회논문지
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• 제25권4호
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• pp.750-755
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• 2021

본 논문에서는 YOLOv4를 이용하여 차량의 부위별 파손현황을 검출하는 기법을 제안한다. 제안 알고리즘은 YOLOv4를 통해 차량의 부위와 파손을 각각 학습시킨 후 검출되는 바운딩 박스의 좌표 정보들을 추출하여 파손과 차량부위의 포함관계를 판단하는 알고리즘을 적용시켜 부위별 파손현황을 도출한다. 또한 성능비교의 객관성을 위하여 동일분야의 VGGNet을 이용한 기법, 이미지 분할과 U-Net 모델을 이용한 기법, Weproove.AI 딥러닝 모델 등을 대조 모델로 포함한다. 이를 통하여 제안 알고리즘의 성능을 비교, 평가하고 검출 모델의 개선 방안을 제안한다.

• 비파괴검사학회지
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• 제35권3호
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• pp.185-192
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• 2015

동적하중에서의 베어링 결함에 대한 실시간 진단기술은 상대적으로 저조하다. 따라서 볼베어링의 이상상태 현상으로 인한 온도 상승 및 진동 증가 등을 사전에 검출하는 기술이 필요하며, 회전체에 대한 운전상태 감시 및 손상 진단을 통해 발전설비의 원활한 운전을 기할 수 있는 검출 기술이 필요하다. 적외선 열화상 실험과 더불어 ANSYS를 이용한 유한요소해석으로부터 실험과 동일한 베어링을 구조 설계 및 해석하여 데이터를 분석함으로써 열화상 기술로 얻은 데이터의 신뢰성을 확보하였다.

• 한국강구조학회 논문집
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• 제14권5호통권60호
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• pp.641-646
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• 2002

본 논문에서는 정적 처짐데이터를 이용한 교량의 강성추정에 관한 기법을 개발하였다. 제안된 기법은 주기적으로 교량의 자중에 의한 처짐을 계측하여 이들 처짐값의 변화량을 이용하여 손상의 진행정도를 규명하는 기법으로써 재하시험이 필요하지 않으며, 최근 활발히 진행되고 있는 계측모니터링 시스템으로부터 획득된 계측데이터를 정량적으로 분석하는 기법으로 활용이 가능하리라 판단된다. 손상평가를 위한 정식화과정에서 부재의 손상은 강성의 저감으로 표현하였으며, 부재의 질량과 초기강성은 실측 또는 도면을 통하여 획득이 가능하다고 가정하였다. 제안된 기법의 타당성 검증을 위하여 수치모형을 통한 손상도 추정결과를 제시하였으며, 기존의 손상도 추정기법 중 비교적 손상에 대한 민감도가 높은 모드형상을 이용한 기법과 손상도 추정결과를 비교하였다. 또한, 손상추정결과를 정확도를 감소시키는 노이즈의 영향을 분석하기 위하여 정적응답자료에 백색잡음을 추가하여 손상도를 추정하였으며, 손상부재의 수가 손상추정 결과에 미치는 영향을 검토하였다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2011년도 정기 학술대회
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• pp.680-683
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• 2011

This study deals with damage detection in beam structure by using modal strain energy-based technique with three different sensor types: accelerometer, lead zirconate titanate (PZT) piezoelectric sensor and electrical strain gage. First, the use of direct piezoelectric effect of PZT sensor for dynamic strain response are presented. Next, a modal strain energy-based damage detection method is outlined. For validation, forced vibration tests are carried out on lab-scale aluminum cantilever beam. The dynamic responses are measured for several damage scenarios. Based on damage localization results, the performance of three different sensor types is evaluated.