Near real-time tracking of dynamic properties for standalone structural health monitoring systems

Automated modal parameter identification of civil engineering structures has been analyzed in a previous paper. An original algorithm, named LEONIDA, working in frequency domain, has been presented and a number of test cases have been discussed in order to point out advantages and drawbacks. It has been demonstrated that LEONIDA represents a promising and reliable tool, in particular for modal testing. Conversely, integration of such a procedure into a fully automated structural health monitoring (SHM) system has shown that it can be used as modal information engine, but length of record durations, amount of computational burden and response time lead to recognize that serious drawbacks and limitations exist for a class of applications, such as continuous monitoring of structures in seismically prone areas.In fact, a fast assessment of relevant structure health conditions in the early post-earthquake phase is becoming of interest in different European areas. In such a context, the statistical treatment of measured dynamic properties could be certainly useful, but it requires the collection of an extensive amount of local and global data in a short time.As a consequence, availability of reliable, robust and fairly fast data processing procedures for modal tracking is fundamental whenever really effective and useful SHM systems are adopted to support civil protection activities during seismic sequences. This applies mainly to strategic structures, whose health conditions must be rapidly assessed after any seismic event, in order to securely manage rescue operations.In the present paper, the main issues related to a fast, robust and reliable modal tracking for emergency management are outlined. Then, an automated modal tracking strategy for SHM applications in earthquake prone regions is described. It is based on the knowledge of the experimental mode shapes and a revised concept of spatial filtering. Results of sample applications of the proposed procedure refer to simulated data and to real measurements collected by a SHM system. The latter are representative of operational conditions and of the transient response due to the ground motion induced by the recent L'Aquila earthquake mainshock. Discussion of results will point out advantages and limitations of the data processing strategy.     

Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring

Discrimination between three different sources of variability in a vibration-based structural health monitoring system is investigated: environmental or operational effects, sensor faults, and structural damage. Separating the environmental or operational effects from the other two is based on the assumption that measurements under different environmental or operational conditions are included in the training data. Distinguishing between sensor fault and structural damage utilizes the fact that the sensor faults are local, while structural damage is global. By localizing the change to a sensor which is then removed from the network, the two different influences can be separated. The sensor network is modelled as a Gaussian process and the generalized likelihood ratio test (GLRT) is then used to detect and localize a change in the system. A numerical and an experimental study are performed to validate the proposed method.     

Evaluation of the health of riveted joints with active and passive structural health monitoring techniques

Many active and passive structural health monitoring (SHM) techniques have been developed for detection of the defects of plates. Generally, riveted joints hold the plates together and their failure may create accidents. In this study, well known active and passive methods were modified for the evaluation of the health of the riveted joints between the plates. The active method generated Lamb waves and monitored their propagation by using lead zirconate titanate (PZT) disks. The signal was analyzed by using the wavelet transformations. The passive method used the Fiber Bragg Grating (FBG) sensors and evaluated the spectral characteristics of the signals by using Fast Fourier Transformation (FFT). The results indicated that the existing methods designed for the evaluation of the health of individual plates may be used for inspection of riveted joints with software modifications.     

基于协方差矩阵遗传算法的飞行器结构健康监控传感器优化配置

利用协方差矩阵改进后的遗传算法研究了飞行器结构健康监控中传感器的优化配置问题。该方法通过历史损伤记录或实验记录对目标结构的损伤概率分布进行量化假设,并结合以协方差矩阵改进后的遗传算法,在传感器位置优化研究中加以考虑,使优化配置后的传感器阵列能够针对目标结构的损伤高发区域进行重点监控。通过与正六边形网格覆盖布局方法在不同传感器数量条件下优化结果的对比分析,证明了该方法的优越性,并对传感器的数量优化问题进行了初步讨论。     

Time-domain analysis of piezoelectric impedance-based structural health monitoring using multilevel wavelet decomposition

A new approach to analyze the response from a piezoelectric wafer in an impedance-based structural health monitoring (SHM) method is proposed. It is shown that the time-domain response of a piezoceramic wafer provides information on the electromechanical impedance (EMI) variation when a monitored structure is damaged. Practical analysis was carried out using wavelet transform in two different levels. This approach simplifies EMI based SHM and the results show that it is more sensitive to damage than methods based on impedance measurements in the frequency domain. The efficiency of this new approach is demonstrated through experiments using an aluminum plate. The piezoelectric wafer was excited using a chirp signal and its response was analyzed using both frequency response functions (FRF) and the proposed method. The results confirm that this new approach is more sensitive to detect damage than FRF based methods.     

Polarization Maintaining Photonic Crystal Fiber sensor embedded in carbon composite for structural health monitoring

Use of Polarization Maintaining Photonic Crystal Fiber (PMPCF) for structural health monitoring (SHM) of composites has been proposed and demonstrated for accurate strain measurement with very negligible temperature sensitivity. Further, this newly proposed sensor has been compared with the conventional Fiber Bragg Grating (FBG) sensor used for SHM. It is established that the embedded PMPCF in carbon composite is the better choice with enhanced strain sensitivity and less temperature sensitivity compared to the embedded FBG sensor.     

An experimental study on damage detection of structures using a timber beam

Using vibration methods for the damage detection and structural health monitoring in bridge structures is rapidly developing. However, very little work has so far been reported on timber bridges. This paper intends to address such shortcomings by experimental investigation on a timber beam using a vibration based method to detect damage. A promising damage detection algorithm based on modal strain energy was adopted and modified to locate/evaluate damage. A laboratory investigation was conducted on a timber beam inflicted with various damage scenarios using modal tests. The modal parameters obtained from the undamaged and damaged state of the test beam were used in the computation of damage index, were then applied using a damage detection algorithm utilising modal strain energy and a statistical approach to detect location of damage. A mode shape reconstruction technique was used to enhance the capability of the damage detection algorithm with limited number of sensors. The test results and analysis show that location of damage can be accurately identified with limited sensors. The modified method is less dependent on the number of modes selected and can detect damage with a higher degree of confidence.     

Design of a real-time overload monitoring system for bridges and roads based on structural response

The problem of the general overload of vehicles that causing premature failure of bridges and roads is becoming more and more obvious. Structural behaviors of bridges and roads need real-time monitoring and diagnosis, timely damage detection, safety evaluation and necessary precautions, in order to prevent accidents such as the crack or the collapse of bridges and roads. However, the existing monitoring system is too expensive to be applied in low budget projects. This paper designs a portable, low-cost, low-power structural monitoring system, which is equipped with Polyvinylidene Fluoride (PVDF) traffic sensors, a central processing unit (CPU), an amplifier circuit, an Analog/Digital (A/D) converter circuit, a communication circuit, an alarm circuit, and analysis software. The proposed system can collect the critical data for a certain judge algorithm about the force of the pavement. An alarm will be signaled and the overweight data will be transmitted to the data center to make further analysis. At the same time, the license plate numbers of the overweight vehicles will be collected and sent to the traffic management department. The system has been tested and the results are satisfactory. Because of its features of simple structure, easy implementation, and low cost, the proposed system fills the application gaps of structural health monitoring in low-budget projects.     

Model error correction from truncated modal flexibility sensitivity and generic parameters. II: experimental verification

This paper reports on the experimental verification of the truncated modal flexibility sensitivity based model updating method proposed in the companion paper (Wu and Law, Mechanical System and Signal Processing, doi: 10.1016/S0888-3270 (03) 00094-3) with real measurements on a three-dimensional cantilever frame structure in the laboratory. Two schemes of model error correction are presented to improve the initial inaccurate finite-element model of the structure. The first procedure updates the initial model in two stages. The systematic model errors are firstly updated using macrogeneric parameters. The local errors are then improved using elemental eigenparameters. The second procedure improves both the systematic and local errors without a measured baseline reference simultaneously. The first procedure can differentiate the types of errors in the structure while the latter procedure removes the usual requirement in most existing model based updating methods that an accurate finite -element model should be used as reference.     

Optical fiber sensors for static and dynamic health monitoring of civil engineering infrastructures: Abode wall case study

Nowadays, structural health monitoring is a fundamental tool to control the civil infrastructures lifetime. Raw earth masonry structures (as adobe, rammed earth, among other techniques) were very common in many regions of the World. With the introduction of reinforced concrete structures in the construction industry, the adoption of traditional masonry systems, and adobe in particular, rapidly declined. However, an important number of the adobe buildings in the world are of cultural, historical and architectural recognized value, presently, about 30% of the World population lives in raw earth buildings.     

大型起重机械结构健康监测技术研究进展

简要介绍ASHM实验室近两年在基于光纤光栅传感器的大型起重机械健康监测技术、基于光纤声发射传感器的大型起重机械局部损伤监测技术,以及基于无线传感节点的大型起重机械健康监测技术研究等方面所取得的阶段性研究成果,部分研究成果已在现场进行应用试验,效果良好。大型起重机械结构健康监测技术不仅能实现对起重机械结构健康状况进行实时监测,还能及时捕获起重机械结构失效的前兆,预防突发性灾难事故,避免重大人员伤亡与财产损失。     

Electrical resistivity as a measure of change of state in substrates: Design, development and validation of an automated system

Intrinsically smart structural composites are materials, which can perform function such as sensing strain, stress damage or temperature. Electrical resistance could potentially serve as an indicator of structural well-being or damage in the structure. To this end, the development of an automated resistance measurement system is desired. An automated nodal electrical resistance acquisition circuitry (NERAC) was designed, and interfaced to a laptop for measurement of electrical resistance/impedance from the substrate of interest. Measurements were carried out using DC/AC method with four-point probe technique. Baseline reading before damage was noted and compared with the resistance measured after damage. The device was calibrated and validated on three different substrates: PVDF samples, composite panels and smart concrete. Results conformed to previous work done on these substrates, validating the effective working of the NERAC device. Change of state of the substrate, after damage was assessed by measurement of resistance/impedance.     

A<Emphasis Type="Italic">built-in</Emphasis> active sensing system-based structural health monitoring technique using statistical pattern recognition

A piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SVM) classifier is developed for railroad track damage identification. Abuilt-in active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage in a railroad track (hole-damage 0.5cm in diameter at the web section and transverse cut damage 7.5cm in length and 0.5cm in depth at the head section). Two damage-sensitive features were separately extracted from each method: a) feature I: root mean square deviations (RMSD) of impedance signatures, and b) feature II: sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining damage indices from these two damagesensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to enhance the damage identification capability of the current active sensing system, a two-step SVM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes (OSH) were successfully established by the two-step SVM classifier: Damage detection was accomplished by the first step-SVM, and damage classification was carried out by the second step-SVM. Finally, the applicability of the proposed two-step SVM classifier has been verified by thirty test patterns prepared in advance from the intact state and two damage states.     

压缩机出口管系振动分析及结构改进

针对某炼油厂压缩机管道在生产中,由于剧烈振动而导致开裂泄漏问题,进行了管系结构模态分析,结果表明管道开裂泄漏的原因为管系结构固有频率与激发主频率相近,形成了共振。共振使得管系薄弱环节处出现裂纹,经过累积损伤和裂纹扩展后,导致管道开裂泄漏。在此基础上提出了在不改变管系主特征条件下,进行减振设计和结构改进。管道结构经改进后,共振现象得到抑制,振幅明显减小,管道的安全性得到了保障。     

Proper orthogonal decomposition based algorithm for detecting damage location and severity in composite beams

A damage detection algorithm based on the proper orthogonal decomposition technique that can be used for structural health monitoring is presented in this study. The proper orthogonal modes are employed as dynamical invariants to filter out the influence of operational/environmental variation in the dynamic response of the structure. Finite element models of a healthy and damaged carbon/epoxy composite beam are used to generate vibration data. Varying levels of stiffness reduction for the elements in the damaged zone of the structure are used to simulate impact damage. Three damage locations (center of beam, fixed end, and free end) with three damage cases for each location are investigated. Different random force inputs are used to introduce variations in the loading conditions of the beam. The results show that the developed algorithm is capable of detecting both location and severity of damage even under changing loading conditions, with a high level of confidence.     

Environmental effects on the identified natural frequencies of the Dowling Hall Footbridge

Continuous monitoring of structural vibrations is becoming increasingly common as sensors and data acquisition systems become more affordable, and as system and damage identification methods develop. In vibration-based structural health monitoring, the dynamic modal parameters of a structure are usually used as damage-sensitive features. The modal parameters are often sensitive to changing environmental conditions such as temperature, humidity, or excitation amplitude. Environmental conditions can have as large an effect on the modal parameters as significant structural damage, so these effects should be accounted for before applying damage identification methods. This paper presents results from a continuous monitoring system installed on the Dowling Hall Footbridge on the campus of Tufts University. Significant variability in the identified natural frequencies is observed; these changes in natural frequency are strongly correlated with temperature. Several nonlinear models are proposed to represent the relationship between the identified natural frequencies and measured temperatures. The final model is then validated using independent sets of measured data. Finally, confidence intervals are estimated for the identified natural frequencies as a function of temperature. The ratio of observed outliers to the expected rate of outliers based on the confidence level can be used as a damage detection index.     

Talkha steel highway bridge monitoring and movement identification using RTK-GPS technique

Monitoring the bridge deformation is the vital task in bridge maintenance and management. Talkha highway steel bridge is one of the two oldest steel bridges in Mansoura city. Nowadays, the Real Time Kinematic-Global Positioning System (RTK-GPS) is capable of providing fast and accurate measurements of bridge oscillations. Also, the movement and damage severity can be identified using the dynamic bridge characteristics obtained from GPS. The aim of the present work is to demonstrate the use of RTK-GPS (1 Hz) to provide data for use in the assessment of existing structures. The moving average filter is used to de-noising the GPS observations. Finite Impulse Response (FIR) with moving average are used to extract the dynamic response and frequency domain of the bridge and Neural Network Auto-Regressive (NNAR) model is used to identify the bridge movement. The results indicate that: (1) the moving average filter is simple and suitable to smooth high noises and errors of GPS observation signals; (2) the multi-filter of short-period can reveal the dynamic displacement of bridge deck movement; (3) the low-frequency movements of the bridge could not be completed and the observation time should be increased to complete it and (4) the movement output of the NNAR is highly conformed with the observation filter.     

Vision-based structural displacement measurement: System performance evaluation and influence factor analysis

In the past decade, the emerging machine vision-based measurement technology has gained great concerns among civil engineers due to its overwhelming merits of non-contact, long-distance, and high-resolution. A critical issue regarding to the measurement performance and accuracy of the vision-based system is how to identify and eliminate the systematic and unsystematic error sources. In this paper, a vision-based structural displacement measurement system integrated with a digital image processing approach is developed. The performance of the developed vision-based system is evaluated by comparing the results simultaneously obtained by the vision-based system and those measured by the magnetostrictive displacement sensor (MDS). A series of experiments are conducted on a shaking table to examine the influence factors which will affect the accuracy and stability of the vision-based system. It is demonstrated that illumination and vapor have a critical effect on the measurement results of the vision-based system.     

基于相控阵的结构损伤范围监测与图像表征

本文针对碳纤维复合材料机翼盒段监测损伤信号微弱难以有效辨识及结构损伤大小的量化问题,提出基于相控阵的碳纤维复合材料结构损伤识别成像与损伤量化方法。利用Lamb波信号在特定方向上的干涉,实现对结构的定向扫描,提高信号的信噪比;通过划分扇环计算其面积进而量化损伤区域大小,同时分析时间延迟执行过程并分析其对损伤识别误差的影响。采用相控阵监测原理在某型无人机碳纤维复合材料机翼盒段上进行实验研究,识别结构中的损伤,对监测结果进行成像,不仅显示结构中损伤的位置并计算量化损伤区域大小,实验研究证明,采用相控阵原理能够有效精确地识别碳纤维复合材料机翼盒段中损伤,图像表征清晰且量化损伤范围准确。