• 풍력에너지저널
• /
• 제9권4호
• /
• pp.16-23
• /
• 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.

• 비파괴검사학회지
• /
• 제29권4호
• /
• pp.338-343
• /
• 2009

토모그래피는 다중 빔을 이용하여 단면을 영상화하는 기법으로서 주로 의료진단 분야에서 인체의 단면 영상획득을 위해 응용되어지는 기법이다. 비파괴검사 분야에서도 단순한 시간영역 신호의 제시에서 탈피하여 검사자에게 영상을 제공함으로써 진단의 효율성을 높이고자 하는 추세이므로 이 기법은 많은 의미를 갖는다. 최근, 유도초음파를 이용한 평판 구조물의 진단 기법이 많은 주목을 받고 있어, 본 논문에서는 컴퓨터 기반 유도초음파 해석 기법과 토모그래피 영상화 기법을 기반으로 2차원 평판에 존재하는 결함 위치를 영상화하는 연구를 수행하였다. 이를 위해 경계요소법을 이용하여 판 구조물에 존재하는 결함이 유도초음파의 전파 양상에 미치는 영향을 해석하고 그 결과를 토모그래피 영상화 기법에 적용하여 평판의 결함 위치를 판별하고자 하였다. 그 결과, 토모그래피를 위해 사용되는 센서의 개수가 결함 검출 성능에 많은 영향을 미침을 확인할 수 있다.

• Smart Structures and Systems
• /
• 제14권2호
• /
• pp.85-104
• /
• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• 비파괴검사학회지
• /
• 제34권6호
• /
• pp.447-456
• /
• 2014

In this paper, a new damage-detection method based on structural vibration is proposed. The essence of the proposed method is the detection of abrupt changes in rotation. Damage-induced rotation (DIR), which is determined from the modal flexibility of the structure, initially occurs only at a specific damaged location. Therefore, damage can be localized by evaluating abrupt changes in rotation. We conducted numerical simulations of two damage scenarios using a 10-story cantilever-type building model. Measurement noise was also considered in the simulation. We compared the sensitivity of the proposed method to localize damage to that of two conventional modal-flexibility-based damage-detection methods, i.e., uniform load surface (ULS) and ULS curvature. The proposed method was able to localize damage in both damage scenarios for cantilever structures, but the conventional methods could not.

• Structural Engineering and Mechanics
• /
• 제83권5호
• /
• pp.593-607
• /
• 2022

To solve the problem of detecting structural damage, a two-stage method using the Kalman filter and Particle Swarm Optimization (PSO) is proposed. In this method, the first PSO population is enhanced using the Kalman filter method based on dynamic responses. Due to noise in the sensor responses and errors in the damage detection process, the accuracy of the damage detection process is reduced. This method proposes a novel approach for solve this problem by integrating the Kalman filter and sensitivity analysis. In the Kalman filter, an approximate damage equation is considered as the equation of state and the damage detection equation based on sensitivity analysis is considered as the observation equation. The first population of PSO are the random damage scenarios. These damage scenarios are estimated using a step of the Kalman filter. The results of this stage are then used to detect the exact location of the damage and its severity with the PSO algorithm. The efficiency of the proposed method is investigated using three numerical examples: a 31-element planer truss, a 52-element space dome, and a 56-element space truss. In these examples, damage is detected for several scenarios in two states: using the no noise responses and using the noisy responses. The results show that the precision and efficiency of the proposed method are appropriate in structural damage detection.

• Smart Structures and Systems
• /
• 제31권6호
• /
• pp.573-583
• /
• 2023

Although unmanned aerial vehicles have been used to overcome the limited accessibility of human-based visual inspection, unresolved issues still remain. Onsite inspectors face difficulty finding previously detected damage locations and tracking their status onsite. For example, an inspector still marks the damage location on a target structure with chalk or drawings while comparing the current status of existing damages to their previous status, as documented onsite. In this study, an augmented-reality-based structural inspection system with onsite damage information marking was developed to enhance the convenience of inspectors. The developed system detects structural damage, creates a holographic marker with damage information on the actual physical damage, and displays the marker onsite via an augmented reality headset. Because inspectors can view a marker with damage information in real time on the display, they can easily identify where the previous damage has occurred and whether the size of the damage is increasing. The performance of the developed system was validated through a field test, demonstrating that the system can enhance convenience by accelerating the inspector's essential tasks such as detecting damages, measuring their size, manually recording their information, and locating previous damages.

• 한국구조물진단유지관리공학회 논문집
• /
• 제24권6호
• /
• pp.206-216
• /
• 2020

국내의 노후 철도교량이 증가함에 따라 노후화로 인한 유지관리비가 점점 증가하고 있으며, 지속적인 관리가 더욱 더 중요해지고 있다. 하지만 관리해야하는 노후 시설물은 증가하지만, 노후 시설물을 점검 및 진단을 할 수 있는 전문 인력은 부족해지고 있다. 이러한 문제를 해결하기 위해 본 연구는 정적 변형률 응답 데이터를 적용하여 AI 기술의 머신러닝 기법으로 구조물의 국부적인 손상을 탐지하는 개선된 학습모델을 제시하고자 한다. 손상탐지 머신러닝 학습 모델을 구성하기 위해 우선 무도상 철도 판형교의 설계도면을 참고하여 교량의 해석모델을 설정하였으며, 설정된 해석모델로 손상시나리오에 따른 정적변형률 데이터를 추출하여 통계적 기법을 이용해 교량의 신뢰도 기반의 Local 손상 지수를 제시하였다. 손상 탐지는 손상 유무 탐지, 크기 탐지, 위치 탐지 3단계의 과정을 수행하여 손상 크기 탐지에서 선형 회귀 모델을 추가로 고려해 임의의 손상을 탐지하였으며, 최종적으로 손상 탐지 머신러닝 분류 학습 모델과 회귀 모델을 이용한 임의의 손상 위치를 추정 및 검증하였다.

• International Journal of Aeronautical and Space Sciences
• /
• 제14권1호
• /
• pp.19-29
• /
• 2013

This paper deals with the study of buckling, vibration, and parametric instability characteristics in a damaged cross-ply and angle-ply laminated plate like beam under in-plane harmonic loading, using the finite element approach. Damage is modelled using an anisotropic damage formulation, based on the concept of reduction in stiffness. The effect of damage on free vibration and buckling characteristics of a thin plate like beam has been studied. It has been observed that damage shows a strong orthogonality and in general deteriorates the static and dynamic characteristics. For the harmonic type of loading, analysis was carried out on a thin plate like beam by solving the governing differential equation which is of Mathieu-Hill type, using the method of multiple scales (MMS). The effects of damage and its location on dynamic stability characteristics have been presented. The results indicate that, compared to the undamaged plate like beam, heavily damaged beams show steeper deviations in simple and combination resonance characteristics.

• Structural Engineering and Mechanics
• /
• 제39권6호
• /
• pp.751-765
• /
• 2011

The present paper deals with the identification of a concentrated damage in an elastic parabolic arch through the minimization of an objective function which measures the differences between numerical and experimental values of static displacements. The damage consists in a notch that reduces the height of the cross section at a given abscissa and therefore causes a variation in the flexural stiffness of the structure. The analytical values of static displacements due to applied loads are calculated by means of the principle of virtual work for both the undamaged and damaged arch. First, pseudo-experimental data are used to study the inverse problem and investigate whether a unique solution can occur or not. Various damage intensities are considered to assess the reliability of the identification procedure. Then, the identification procedure is applied to an experimental case, where displacements are measured on a prototype arch. The identified values of damage parameters, i.e., location and intensity, are compared to those obtained by means of a dynamic identification technique performed on the same structure.

• Structural Monitoring and Maintenance
• /
• 제5권3호
• /
• pp.379-390
• /
• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.