Remote structural health monitoring with serially multiplexed fiber optic acoustic emission sensors

Development and testing of a serially multiplexed fiber optic sensor system is described.The sensor differs from conventional fiber optic acoustic systems,as it is capable of sensing AE emissions at several points along the length of a single fiber.Multiplexing provides for single channel detection of cracks and their locations in large structural systems. An algorithm was developed for signal recognition and tagging of the AE waveforms for detection of' crack locations,Labora- tory experiments on plain concrete beams and post-tensioned FRP tendons were pcrlormed to evaluate the crack detection capability of the sensor system.The acoustic emission sensor was able to detect initiation,growth and location of the cracks in concrete as well as in the FRP tendons.The AE system is potentially suitable lot applications involving health monitoring of structures following an earthquake.

Effects of excitation frequency on detection accuracy of orthogonal wavelet decomposition for structural health monitoring

Accurate estimation of stiffness loss is a challenging problem in structural health monitoring.In this studyorthogonal wavelet decomposition is used for identifying the stiffness loss in a single degree of freedom spring-mass-dampersystem.The effects of excitation frequency on accuracy of damage detection is investigated.Results show that pseudo-aliaseffects caused by the orthogonal wavelet decomposition(OWD),affect damage detectability.It is demonstrated that theproposed approach is sunable for damage detection when the excitation frequency is relatively low.This study shows how apriori knowledge about the signal and ability to control the sampling frequency can enhance damage detectability.

Time Series Analysis for Vibration-Based Structural Health Monitoring:A Review

Structural health monitoring(SHM)is a vast,interdisciplinary research field whose literature spans several decades with focusing on condition assessment of different types of structures including aerospace,mechanical and civil structures.The need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based inspection.Statistical time series methods for SHM form an important and rapidly evolving category within the broader vibration-based methods.In the literature on the structural damage detection,many time series-based methods have been proposed.When a considered time series model approximates the vibration response of a structure and model coefficients or residual error are obtained,any deviations in these coefficients or residual error can be inferred as an indication of a change or damage in the structure.Depending on the technique employed,various damage sensitive features have been proposed to capture the deviations.This paper reviews the application of time series analysis for SHM.The different types of time series analysis are described,and the basic principles are explained in detail.Then,the literature is reviewed based on how a damage sensitive feature is formed.In addition,some investigations that have attempted to modify and/or combine time series analysis with other approaches for better damage identification are presented.

Output only modal identification and structural damage detection using time frequency & wavelet techniques

The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time variant （LTV--due to damage） systems based on Time-frequency （TF） techniques--such as short-time Fourier transform （STFT）, empirical mode decomposition （EMD）, and wavelets--is proposed. STFT, EMD, and wavelet methods developed to date are reviewed in detail. In addition a Hilbert transform （HT） approach to determine frequency and damping is also presented. In this paper, STFT, EMD, HT and wavelet techniques are developed for decomposition of free vibration response of MDOF systems into their modal components. Once the modal components are obtained, each one is processed using Hilbert transform to obtain the modal frequency and damping ratios. In addition, the ratio of modal components at different degrees of freedom facilitate determination of mode shape. In cases with output only modal identification using ambient/random response, the random decrement technique is used to obtain free vibration response. The advantage of TF techniques is that they arc signal based; hence, can be used for output only modal identification. A three degree of freedom 1：10 scale model test structure is used to validate the proposed output only modal identification techniques based on STFT, EMD, HT, wavelets. Both measured free vibration and forced vibration （white noise） response are considered. The secondary objective of this paper is to show the relative ease with which the TF techniques can be used for modal identification and their potential for real world applications where output only identification is essential. Recorded ambient vibration data processed using techniques such as the random decrement technique can be used to obtain the free vibration response, so that further processing using TF based modal identification can be performed.

Application of wavelet transform in structural health monitoring

Structural health monitoring(SHM)is a process of implementing a damage detection strategy in existing structures to evaluate their condition to ensure safety.The changes in the material,geometric and/or structural properties affect structural responses,which can be captured and analyzed for condition assessment.Various vibration-based damage detection algorithms have been developed in the past few decades.Among them,wavelet transform(WT)gained popularity as an efficient method of signal processing to build a framework to identify modal properties and detect damage in structures.This article presents the state-of-the-art implementation of various WT tools in SHM with a focus on civil structures.The unique features and limitations of WT,and a comparison of WT and other signal processing methods,are further discussed.The comprehensive literature review in this study will help interested researchers to investigate the use of WT in SHM to meet their specific needs.

Structural Health Monitoring by Accelerometric Data of a Continuously Monitored Structure with Induced Damages

The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.

Development of temperature-robust damage factor based on sensor fusion for a wind turbine structure

Wind power systems have gained much attention due to the relatively high reliability, maturity in technology and cost competitiveness compared to other renewable alternatives. Advances have been made to increase the power efficiency of the wind turbines while less attention has been focused on structural integrity assessment of the structural systems. Vibration-based damage detection has widely been researched to identify damages on a structure based on change in d^mmic characteristics. Widely spread methods are natural frequency-based, mode shape-based, and curvature mode shape-based methods. The natural frequency-based methods are convenient but vulnerable to environmental temperature variation which degrades damage detection capability; mode shapes are less influenced by temperature variation and able to locate damage but requires extensive sensor instrumentation which is costly and vulnerable to signal noises. This study proposes novelty of damage factor based on sensor fusion to exclude effect of temperature variation. The combined use of an accelerometer and an inclinometer was considered and damage factor was defined as a change in relationship between those two measurements. The advantages of the proposed method are： 1） requirement of small number of sensor, 2） robusmess to change in temperature and signal noise and 3） ability to roughly locate damage. Validation of the proposed method is carried out through numerical simulation on a simplified 5 MW wind turbine model.

Damage detection with image processing: a comparative study

Large structures,such as bridges,highways,etc.,need to be inspected to evaluate their actual physical and functional condition,to predict future conditions,and to help decision makers allocating maintenance and rehabilitation resources.The assessment of civil infrastructure condition is carried out through information obtained by inspection and/or monitoring operations.Traditional techniques in structural health monitoring(SHM)involve visual inspection related to inspection standards that can be time-consuming data collection,expensive,labor intensive,and dangerous.To address these limitations,machine vision-based inspection procedures have increasingly been investigated within the research community.In this context,this paper proposes and compares four different computer vision procedures to identify damage by image processing:Otsu method thresholding,Markov random fields segmentation,RGB color detection technique,and K-means clustering algorithm.The first method is based on segmentation by thresholding that returns a binary image from a grayscale image.The Markov random fields technique uses a probabilistic approach to assign labels to model the spatial dependencies in image pixels.The RGB technique uses color detection to evaluate the defect extensions.Finally,K-means algorithm is based on Euclidean distance for clustering of the images.The benefits and limitations of each technique are discussed,and the challenges of using the techniques are highlighted.To show the effectiveness of the described techniques in damage detection of civil infrastructures,a case study is presented.Results show that various types of corrosion and cracks can be detected by image processing techniques making the proposed techniques a suitable tool for the prediction of the damage evolution in civil infrastructures.

Fundamental modeling issues on benchmark structure for structural health monitoring

The IASC-ASCE Structural Health Monitoring Task Group developed a series of benchmark problems, and participants of the benchmark study were charged with using a 12-degree-of-freedom (DOF) shear building as their identification model. The present article addresses improperness, including the parameter and modeling errors, of using this particular model for the intended purpose of damage detec- tion, while the measurements of damaged structures are synthesized from a full-order finite-element model. In addressing parameter errors, a model calibration procedure is utilized to tune the mass and stiffness matrices of the baseline identification model, and a 12-DOF shear building model that preserves the first three modes of the full-order model is obtained. Sequentially, this calibrated model is employed as the baseline model while performing the damage detection under various damage scenarios. Numerical results indicate that the 12-DOF shear building model is an over-simplified identification model, through which only idealized damage situations for the benchmark structure can be detected. It is suggested that a more sophisticated 3-dimensional frame structure model should be adopted as the identification model, if one intends to detect local member damages correctly.

Outlier Detection and Forecasting for Bridge Health Monitoring Based on Time Series Intervention Analysis

The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research direction for bridge state assessment.However,outliers inevitably exist in the monitoring data due to various interventions,which reduce the precision of model fitting and affect the forecasting results.Therefore,the identification of outliers is crucial for the accurate interpretation of the monitoring data.In this study,a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory,and the forecasting of the structural responses is carried out.There are three techniques that we focus on:(1)the modeling of seasonal autoregressive integrated moving average(SARIMA)model;(2)the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown;(3)forecasting of the model with outlier effects.The method was tested with a case study using monitoring data on a real bridge.The establishment of the original SARIMA model without considering outliers is first discussed,including the stationarity,order determination,parameter estimation and diagnostic checking of the model.Then the time-by-time iterative procedure for outlier detection,which is implemented by appropriate test statistics of the residuals,is performed.The SARIMA-outlier model is subsequently built.Finally,a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out.The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data.When the influence of outliers is taken into account,the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.

Seismic Health Monitoring of Foundations Using Artificial Neural Networks

Damage identification plays an important role in structural health monitoring systems. Despite variety in damage identification methods, little attention has been paid to the seismic damage identification of foundations. When shear walls serve as the lateral load resistance system of structures, foundations may subject to the high level of concentrated moment and shear forces. Consequently, they can experience severe damage. Since such damage is often internal and not visible, visual inspections cannot identify the location and the severity of damage. Therefore, a robust method is required for damage localization and quantification of foundations. According to the concept of performance-based seismic design of structures, the seismic behavior of foundations is considered as Force-Controlled. Therefore, for damage identification of foundation, internal forces should be estimated during ground motions. In this study, for real-time seismic damage detection of foundations, a method based on artificial neural networks was proposed. A feed-forward multilayer neural network with one hidden layer was selected to map input samples to output parameters. The lateral displacements of stories were considered as the input parameters of the neural network while moment and shear force demands at critical points of foundations were taken into account as the output parameters. In order to prepare well-distributed data sets for training the neural network, several nonlinear time history analyses were carried out. The proposed method was tested on the foundation of a five-story concrete shear wall building. The obtained results revealed that the proposed method was successfully estimated moment and shear force demands at the critical points of the foundation.

Disbond detection with piezoelectric wafer active sensors in RC structures strengthened with FRP composite overlays

The capability of embedded piezoelectric wafer active sensors(PWAS)to perform in-situ nondestructive evaluation(NDE)for structural health monitoring(SHM)of reinforced concrete(RC)structures strengthened with fiber reinforced polymer(FRP)composite overlays is explored.First,the disbond detection method were developed on coupon specimens consisting of concrete blocks covered with an FRP composite layer.It was found that the presence of a disbond crack drastically changes the electromecfianical(E/M)impedance spectrum lneasurcd at the PWAS terlninals.The spectral changes depend on the distance between the PWAS and the crack tip.Second,large scale experiments were conducted on a RC beam strengthened with carbon fiber reinforced polymer(CFRP)composite overlay.The beam was subject to an accelerated fatigue load regime in a three-point bending configuration up to a total of 807,415 cycles.During these fatigue tests,the CFRP overlay experienced disbonding beginning at about 500,000 cycles.The PWAS were able to detect the disbonding before it could be reliably seen by visual inspection.Good correlation between the PWAS readings and the position and extent of disbond damage was observed.These preliminary results demonstrate the potential of PWAS technology for SHM of RC structures strengthened with FRP composite overlays.

Damage Detection of Offshore Jacket Structures Using Frequency Domain Selective Measurements

The development of damage detection techniques for offshore jacket structures is vital to prevent catastrophic events. This paper applies a frequency response based method for the purpose of structural health monitoring. In efforts to fulfill this task, concept of the minimum rank perturbation theory has been utilized. The present article introduces a promising methodology to select frequency points effectively. To achieve this goal, modal strain energy ratio of each member was evaluated at different natural frequencies of structure in order to identify the sensitive frequency domain for damage detection. The proposed methodology opens up the possibility of much greater detection efficiency. In addition, the performance of the proposed method was evaluated in relation to multiple damages. The aforementioned points are illustrated using the numerical study of a two dimensional jacket platform, and the results proved to be satisfactory utilizing the proposed methodology.

Damage Detection in Reinforced Concrete Berthing Jetty Using a Plasticity Model Approach

A conventional method of damage modeling by a reduction in stiffness is insufficient to model the complex non-linear damage characteristics of concrete material accurately.In this research,the concrete damage plasticity constitutive model is used to develop the numerical model of a deck beam on a berthing jetty in the Abaqus finite element package.The model constitutes a solid section of 3D hexahedral brick elements for concrete material embedded with 2D quadrilateral surface elements as reinforcements.The model was validated against experimental results of a beam of comparable dimensions in a cited literature.The validated beam model is then used in a three-point load test configuration to demonstrate its applicability for preliminary numerical evaluation of damage detection strategy in marine concrete structural health monitoring.The natural frequency was identified to detect the presence of damage and mode shape curvature was found sensitive to the location of damage.

飞机结构健康监测策略研究

飞机全寿命周期结构健康监测技术的应用对提高飞机在役安全性和可靠性,提升地面维护效率,延长飞机使用寿命具有巨大的价值和效益,因而得到国内外的持续关注和研究。该技术在飞机上的应用已成为衡量飞机先进性的重要指标之一,而飞机结构健康监测标准规范与策略的制定是技术开展的首要任务。分析了航空制造业先进国家结构健康监测技术相关的标准规范,明确了制定标准规范的具体要求;通过总结国外结构健康监测技术最新研究进展,梳理了结构健康监测技术框架体系;针对我国飞机结构地面试验和服役环境2大应用场景的技术特点,明晰了我国结构健康监测的技术需求,并在此基础上,提出了我国飞机结构健康监测的总体架构和可供剪裁的技术实施策略。

基于跨模型模态应变能变化的结构损伤识别两阶段法

为提高结构损伤识别精度,采用跨模型模态应变能变化(Cross-model Modal Strain Energy Changes,CMSEC)代替传统的模态应变能变化(Modal Strain Energy Changes, MSEC)建立先定位后定量的结构损伤识别两阶段法.与传统模态应变能(Modal Strain Energy, MSE)计算时仅利用结构损伤后的振型不同,跨模型模态应变能(CMSE)的计算同时利用结构损伤前后的振型.先利用CMSEC构建CMSECI (Cross-model Modal Strain Energy Index)损伤指标来确定损伤位置,筛选出疑似损伤构件后,再利用CMSEC建立定量识别损伤程度的灵敏度方程.采用基于奇异值截断的迭代法求解该方程,以避免迭代过程中可能出现的病态方程问题.根据公式构成可以定性判断CMSEC比MSEC更难被噪声干扰.某简支梁算例表明,本文方法在损伤定位和损伤定量两个阶段均比传统方法具有更高的识别精度.为了深入考察本文方法的优越性,采用蒙特卡洛法从宏观的相对误差上界和具体的相对误差分布这两个视角解释了本文方法优于传统方法的原因.

基于灰度分析的桥梁健康监测系统传感器异常检测

为了能够及时检测出传感器的异常状况,设计了一种基于灰色关联度分析的桥梁健康监测系统传感器异常检测模型.首先,分别对多个应变传感器在正常工作和单传感器异常时采集的数据进行灰色关联度分析,得到表征每一列传感器数据与其余列数据在序列几何形状上相似程度的最不相关次数序列.通过对比发现无异常和有异常时最不相关次数的分布呈现明显差异,从而验证了此方法的可行性;然后,设计了一种权重计算策略,将最不相关次数序列转化为归一化的值,并将其作为评价指标,以此来量化每一列传感器数据与其他列数据之间的相关程度;最后,通过分析多组应变数据的评价指标,设置了多阈值预警机制,以实现对不同程度的异常情况做出相应的判定.在另一组加速度监测数据上模拟多种异常程度不同的异常状况并进行检测,结果显示整体的异常识别率在90%以上.

基于数据合成的飞行器结构损伤状态快速识别方法

针对当前飞行器结构健康监测过程中存在的识别流程复杂、识别准确度低的问题,提出一种基于数据合成的飞行器结构损伤状态快速识别方法。建立数字孪生结构损伤快速识别模型构建流程,构建飞行器结构数字模型。基于数据合成思想,提出一种对传感器数据的可信度评价方法,建立具有可解释性的生成-判别模型,解决了因样本数据不足导致的学习准确率低的问题。引入分类边界模糊化方法,使用判别模型确定模糊化区域以提升神经网络识别的稳定性。最后,以某无人机为例,对所提识别方法进行验证。结果表明,该方法能够高效构建结构损伤状态数据库并提升识别泛化能力和稳定性,对损伤状态的识别准确率超过99%。

结构损伤识别的二次反馈岭估计法

为了克服灵敏度方程的病态最小二乘问题,提出一种基于模态柔度变化的结构损伤识别二次反馈岭估计法。采用模态特征值和振型来组集模态柔度矩阵,建立由模态柔度变化求解单元损伤系数的损伤识别线性方程组。在损伤定位阶段,先分别利用非负最小二乘法和反馈岭估计法来初步评估损伤位置。由于这两种方法的损伤定位结果常受到不同未损单元的干扰,所以通过对它们的计算结果取交集来综合确定疑似损伤单元。在损伤定量阶段,将正则化矩阵中对应疑似损伤单元的对角元素缩小,同时将对应其余单元的对角元素放大,进行二次反馈岭估计以最终完成损伤定量识别。某简支梁数值分析表明,相比非负最小二乘法和反馈岭估计法,所提方法具有更强的抗噪鲁棒性。无论是损伤定位还是损伤定量,均具有更加可靠的识别精度。

航天器结构中导波健康监测技术的若干进展

基于航天器结构健康监测需求,从空间碎片撞击定位、结构损伤评估、泄漏定位3个方面,阐述了导波的相关应用进展。首先分析了空间站、可重复使用运载器和燃料贮箱等结构面临的风险和监测需求,然后介绍了相关的理论和监测方法研究进展,以及多物理场导波建模和传播仿真技术,最后探讨了设计集成“撞、漏、损”多功能一体化结构健康监测系统的实现思路,以及导波结构健康监测技术在与空间机器人、空间激光溯源、无线通信和能量传输结合方面的应用前景。