Health Monitoring Of Large Structures Research Articles

Robust and efficient feature-based method for structural health monitoring of large structures

Robust and efficient feature-based method for structural health monitoring of large structures

Research on a bimetallic-sensitized FBG temperature sensor.

Temperature measurement is of great significance for research in the health monitoring of large structures and earthquake precursors. Against the frequently reported low sensitivity of fiber Bragg grating (FBG) temperature sensors, a bimetallic-sensitized FBG temperature sensor was proposed. The sensitization structure of the FBG temperature sensor was designed, and the sensor sensitivity was analyzed; the lengths and materials of the substrate and strain transfer beam were analyzed theoretically; 7075 aluminum and 4J36 invar were chosen as bimetallic materials, and the ratio of the substrate length to the sensing fiber length was determined. The structural parameters were optimized; the real sensor was developed, and its performance was tested. The results suggested that the sensitivity of the FBG temperature sensor was 50.2 pm/°C, about five times than that of a bare FBG sensor, and its linearity was more than 0.99. The findings offer a reference for developing sensors of the same type and further improving the sensitivity of the FBG temperature sensors.

Use of digital image correlation and machine learning for the optimal strain placement in a full-scale composite tidal turbine blade

One of the challenges testing and health monitoring of large structures represents is getting as much information as possible from a specimen with a limited number of sensors. In this work, a data-driven approach was pursued to decide the optimal location of single-point strain gauges using machine learning algorithms (MLA) and information from Digital Image Correlation (DIC) measurements. The optimal strain gauge placement was computed for a range of sensor numbers and the presence of sensors in the high-gradient regions was identified. Strain maps of almost 40,000 measurements were reconstructed successfully with fewer than twenty measured values using the method employed. However, certain loss of image contrast was identified which is likely to have resulted from the treatment of non-numerical values.

Structural Health Monitoring of Large Structures via mmWave Sensing

Abstract Vibration and deformation measurement are of great significance for structural health monitoring (SHM). While the existing accelerometer, laser and vision-based approaches have limitations in multi-point vibration measurement, efficiency and environmental adaptability, especially for large structures. In this paper, we present a method for structural health monitoring with multi-point vibration and deformation measurement via millimetre- wave sensing. First, we describe the mathematical model and the principle of multi-target synchronous vibration and deformation monitoring. Second, with a built prototype of mmWave sensing system, we evaluate our method with both laboratory and field tests. The experimental results of different amplitudes, frequencies and velocities show that our approach can accurately monitor the vibration and deformation of multi-point with μm-level accuracy. The field test of Shanghai Songpu Bridge is provided to further validate the effectiveness of the method, offering a novel approach for contactless vibration and deformation monitoring of large structures in SHM.

Theory and method of multi-point synchronous deformation and vibration measurement based on millimeter-wave sensing

Although accelerometer-, vision-, and laser-based sensing technologies are widely used in deformation and vibration measurements, the multi-point simultaneous deformation and vibration measurement of large-scale structures with high accuracy remains a challenge. Herein, we developed a novel technology for contactless deformation and vibration measurement using millimeter-wave (mmWave) linear frequency-modulated continuous wave radar, establishing the theory and method for multi-point simultaneous deformation and vibration measurement based on mmWave sensing. The principle of mmWave multi-point vibration measurement and a method of displacement extraction were systematically described using the baseband signal model of multi-target perception. We built a prototype of the proposed mmWave vibration measurement system and a platform for simulated vibration tests. The accuracy of the mmWave multi-point vibration measurement technology was verified and compared with that of laser displacement sensors. Using the prototype system, we conducted experiments on the multi-point synchronous vibration measurement and model identification of the slender hinged flexible rod structure of a certain aerospace equipment in China and then described the dynamic response monitoring of a large bridge structure. Results showed that the deformation and vibration measurement method based on mmWave sensing developed in this work could conveniently and accurately extract the time domain information of multi-target vibration displacements. Our work provides an innovative method and approach for non-contact multi-point synchronous deformation and vibration measurements that could be applied to the mechanical performance test and health monitoring of large structures.

SHM System and a FEM Model-Based Force Analysis Assessment in Stay Cables.

The Rędziński Bridge in Wrocław is the biggest Polish concrete cable-stayed bridge. It is equipped with a large structural health monitoring (SHM) system which has been collecting the measured data since the bridge opening in the year 2011. This paper presents a comparison between the measured data and the finite element method (FEM) calculations, while taking into account 7 years of data collection and analyses. The first part of this paper concerns the SHM application. In the next part, which contains comparisons between forces in cables and temperature changes throughout the structure, the measured data are presented. The third part includes SHM-based calculations and simulations with a complex FEM model to check the measured data and to estimate future measurements. The last part contains a durability assessment calculation for the cable stays.

Smart nano-engineered cementitious composite sensors for vibration-based health monitoring of large structures

Cement composites, generally with high resistivity, when incorporated with conductive nanoparticles, are found to be capable of reflecting strain state due to the change in electrical resistance of the matrix. These unique properties of cement-based nanocomposites (CNC) allow the development of smart cement sensors for continuous health monitoring of large concrete structures. Due to the limitation of strain-based sensing (for local behaviour), vibration-based sensing is gaining considerable importance. The performance of cement-based nanocomposites, unlike under the static load or slow varying load, for dynamic response sensing is very scanty. In the present study, an attempt has been made to develop robust smart cement sensors for capturing the multiple natural frequencies of a structure. First, a systemic way of fabrication of sensors (smart cement sensors) using cement matrix and nano-inclusions of carbon nanotubes is presented. Three different types of multi-walled nanotubes (MWCNTs) like pristine MWCNTs (P-MWCNTs), carboxyl acid (−COOH) MWCNTs and hydroxyl (−OH) MWCNTs with four different weight percentage (0.1-0.75 wt.%) of cement matrix are considered for the study. Strain sensibility of the developed sensors is explored under cyclic compression load. Further, the efficacy of the developed sensor for dynamic sensing is investigated using a large reinforced concrete bridge girder where instrumented impulse hammer is used to dynamically excite the bridge girder. It is found that the developed CNC is capable of capturing the natural frequencies (even up to the third mode) which are found to be sufficient for vibration-based health monitoring of structures. The performance of the developed sensors is compared with off-the-shelf accelerometers and is found to be in good agreement. The study provides a demonstrable technology for using the CNC as a vibration sensor for continuous health monitoring and assessment of structures, just by using ambient vibration information.

Guided wave sensitivity prediction for part and through-thickness crack-like defects

Guided waves allow for the efficient structural health monitoring of large structures using phased or distributed arrays of sensors. The sensitivity for specific defects can be improved by accounting for the angular scattering pattern. The scattering of the fundamental anti-symmetric guided wave mode (A0 Lamb mode) at through-thickness and part-through crack-like defects was studied experimentally and from three-dimensional finite element simulations. Experimentally, the scattered field around manufactured notches of different depths and lengths in an aluminium plate was measured using a laser interferometer. The scattered field was extracted by taking the complex difference in the frequency domain between baseline measurement and measurements around the defect. Good agreement was found between measurements and three-dimensional finite element simulations, and the amplitude and directionality pattern of the scattered field can be predicted accurately. The angular directionality pattern of the scattered field depending on the direction of the incident wave relative to the crack-like defect orientation, depth and length relative to the wavelength was investigated. For short and part-thickness defects, the main scattering effect is a reduction of the (forward) wave propagating past the defect with very limited backscattered amplitude. Significant energy scattered back towards the incident wave direction was only found for perpendicular incidence on long and deep defects. Even for large defects, almost no energy is scattered in certain directions from the defect, possibly complicating defect detection. Based on the predicted amplitude and angular dependency of the scattered guided waves, the sensitivity for defect detection using localized and distributed structural health monitoring sensor array systems can be quantified.

Structural Health Monitoring of Large Structures Using Acoustic Emission–Case Histories

Acoustic emission (AE) techniques have successfully been used for assuring the structural integrity of large rocket motorcases since 1963 [...]

Strain transfer mechanism of grating ends fiber Bragg grating for structural health monitoring

The grating ends bonding fiber Bragg grating (FBG) sensor has been widely used in sensor packages such as substrate type and clamp type for health monitoring of large structures. However, owing to the shear deformation of the adhesive layer of FBG, the strain measured by FBG is often different from the strain of actual matrix, which causes strain measurement errors. This investigation aims at improving the measurement accuracy of strain for the grating ends surface-bonded FBG. To fulfill this objective, a strain transfer equation of the grating ends bonding FBG is derived, and a theoretical model of the average strain transfer from the matrix to the optical fiber is developed. Moreover, parameters that influence the average strain transfer rate from the matrix to the optical fiber are analyzed. A selection scheme of bonding parameters by numerical simulation is provided, which is significantly advantageous over that of the grating bonding FBG. The theoretical equation is verified by finite element method (FEM). Compared with the existing model, the proposed model has higher measurement accuracy. Experimental tests are performed to validate the effectiveness of the proposed model on the equal-intensity cantilever beam, whose surface is attached to the bare FBG with grating ends bonding and strain gauge by using epoxy glue. The results show that there is a great agreement between the outcome of the bare FBG and that of the strain gauge, and the corrected strain is closer to the true strain. The proposed model provides a theoretical basis for the design of the grating ends surface-bonded FBG strain sensor for health monitoring of large structures.

Interview

Prof. Zhike Peng from Shanghai Jiao Tong University, talks to Electronics Letters about the background to his paper ‘Static clutter elimination for FMCW radar displacement measurement based on phasor offset compensation’, page 1491. Prof. Zhike Peng Currently, part of my research focuses on signal processing, microwave detection, and micro-Doppler analysis, in particular for microwave/RF radar vibration monitoring, biomedical, and some other industrial applications. To realise non-contact vibration and movement monitoring, we found microwave radar to be a good approach, as it is low-cost and has high robustness against harsh environments. Fortunately, benefiting from the technical advances in high-frequency printed circuit board and hardware miniaturisation technology, microwave radar modules are widely used in various applications. Therefore, we try to use microwave radar, as an effective short-range sensor to perform non-contact displacement tracking. Specifically, we use frequency-modulated continuous-wave (FMCW) radar to measure the displacement variation in vibration motion. Currently, using phase-based radar interferometry technique, FMCW radar has excellent capacity to measure small displacement variation. However, since the radar antenna has a finite radiation beam-width, i.e. it cannot be a point, all targets within the radar radiation coverage reflect echoes and the radar baseband signal is the sum of multiple components. Generally, FMCW radar can isolate different targets with a different range bin, but struggles to isolate components generated by targets in the same bin. Reflections from stationary objects (i.e. static clutter) near the desired target interfere with the wanted signals backscattering from the moving scatterers at the same range, causing the estimated displacement time series to be smaller than the truth. When the stationary object and the desired target have comparable reflective performance, the static clutter interference has a significant impact on the estimation accuracy. In our Letter, a post-processing procedure based on phasor offset compensation (POC) is proposed to eliminate the static clutter interference. It can be implemented easily by using an updated signal processing algorithm and achieves accurate displacement measurement. Previously, for this problem existing works mainly focus on the hardware side, such as installing a strong reflection point, or using frequency-shift transponders, which increase hardware complexity and is limited in real-life applications. Comparatively, our POC technique is a novel way to solve the static clutter interference problem from a signal processing side. This work can be directly applied in FMCW radar displacement measurement, e.g. vibration monitoring, large structural health monitoring, slow movement detection and vital sign detection. In the long run, the POC technique can be extended in other phase-based measurement system for a wide variety of applications. Currently, one portion of our research group is working on radar signal processing algorithms to achieve good performance in related applications. Specifically, we are now working on the research of non-contact vibration monitoring and structural health monitoring based on FMCW radar, and micro-Doppler analysis for pattern recognition. In the near future, we would like to extend our research in radar imaging and environmental perception. Then, we would like to investigate the information fusion technique combined with the camera, to obtain better and more intelligent environmental imaging and recognition. In the next 10 years, we would like to see more integrated software defined radar developing platforms and advanced radar signal processing algorithms for a variety of applications. Moreover, we expect to see intelligent radar detection systems with multiple type radars. Various sensors have their own advantages and drawbacks, therefore we need to see intelligent information fusion techniques to achieve better environmental imaging and recognition. This includes object recognition and physical quantity measurement, which is greatly beneficial to autopilot, robot vision and other fields. We would like to develop advanced signal processing methods, intelligent information fusion techniques and several prototypes of intelligent sensor to boost the development in this field.

Decomposing guided wavefields with dictionary learning

Ultrasonic guided waves have become a powerful tool for the structural health monitoring of large structures due to their low attenuation and high damage sensitivity. Yet, one significant challenge of working with guided waves is their complexity. Ultrasonic guided waves demonstrate strong multi-modal and dispersive characteristics and are often used in structures with many boundaries and reflecting elements. In prior work, compressive sensing theory and sparse recovery algorithms have been used to learn the multi-modal and dispersive properties of guided waves based on relatively simple wave models. The algorithms can predict and construct wavefields based on these learned properties. Furthermore, this predictive power can be used to detect and locate damage in a structure. However, these algorithms cannot yet predict and utilize reflections as well as other complexities (such as inhomogeneities) in a structure due to the simplicity of the current model. This paper presents a dictionary learning approach to learn this model directly from simulation or experimental data. This paper focuses on studying the models (i.e., dictionaries) created by dictionary learning under various simulated conditions. We demonstrate that we can use these dictionaries to accurately predict wavefields when waves are distorted by reflections, velocity inhomogeneities, and velocity anisotropy.

Self-Diagnosis and Self-Reconfiguration of Piezoelectric Actuator and Sensor Network for Large Structural Health Monitoring

For large structural health monitoring in practical applications, the sensor failure in large sensor network will generate the failure of the damage evaluation or the leaving of unmonitored area in a large structure. Hence, in order to realize a real-time and reliable evaluation for the large structural damage and satisfy the requirement of advanced maintenance of the structure, the paper introduces an efficient piezoelectric sensor failure monitoring approach integrated with self-diagnosis and self-reconfiguration reasoning method. Firstly, the developed method is introduced for large structural health monitoring. Then, the effects of piezoelectric actuator and sensor bonding defects on Lamb wave propagations are investigated in a real-world monitoring experiment for the large aviation aluminum plate. Lastly, the self-diagnosis and self-reconfiguration method of piezoelectric actuator and sensor network for large structural health monitoring based on Lamb wave for the passive and active damage monitoring is verified in the experiment. Based on the results shown in the experiment, a big potential in real-time application demonstrates that the method can accurately and rapidly identify the sensor failure by reasoning and analyzing Lamb wave propagations using proposed methods.

Statistical updating of finite element model with Lamb wave sensing data for damage detection problems

Health monitoring of large structures with embedded, distributed sensor systems is gaining importance. This study proposes a new probabilistic model updating method in order to improve the damage prediction capability of a finite element analysis (FEA) model with experimental observations from a Lamb-wave sensing system. The approach statistically calibrates unknown parameters of the FEA model and estimates a bias-correcting function to achieve a good match between the model predictions and sensor observations. An experimental validation study is presented in which a set of controlled damages are generated on a composite panel. Time-series signals are collected with the damage condition using a Lamb-wave sensing system and a one dimensional FEA model of the panel is constructed to quantify the damages. The damage indices from both the experiments and the computational model are used to calibrate assumed parameters of the FEA model and to estimate a bias-correction function. The updated model is used to predict the size (extent) and location of damage. It is shown that the proposed model updating approach achieves a prediction accuracy that is superior to a purely statistical approach or a deterministic model calibration approach.

Structural damage detection from transient responses using square-root unscented filtering

Health monitoring of large structures is inherently difficult due to the relatively small number of available sensors/measurements that can be made within budgetary constraints. To accurately detect the presence of damage in a structure requires a reliable model, or at least a good representation of the structure prior to damage. Approaches to detecting and localizing damage are predominantly based on either frequency changes or transient responses. Transient or closed-loop responses are available more readily during operation and appear to be more suitable for online damage detection than approaches based on frequency changes. In order to detect damage in a large structure, the structural characteristics such as mass matrices and stiffness matrices need to be estimated. This paper utilizes an implementation of the unscented Kalman filter in square-root form to estimate changes to the system mass/stiffness. The damage detection problem is solved online by updating the structural parameter estimates using a limited amount of measurement data. Example results are presented for spring–mass, beam and truss structures where the only measurements are accelerometer data from a limited number of nodes. The numerical results show that the approach is capable of detecting changes in the structure from the outputs online.

Measurement and Analysis of Wind-Induced Response of Tall Building Based on GPS Technology

The Global Positioning System (GPS) has been used in the health monitoring of large structures in recent years since it is weather independent, capable of autonomous operation, and does not require a line-of-sight between target points. In this paper, a structural health monitoring system based on the GPS is devised for a high-rise building. The field data, such as wind speed, wind direction and displacement responses were simultaneously and continuously measured under strong wind conditions. The relation between velocity components of the atmospheric wind and pressure fluctuations on the roof of the building are analyzed using the wavelet scalogram and wavelet coherence method, and a statistical significance level estimation method based on Monte Carlo simulation is presented to isolate the meaningful coherence from the spurious noise map. Finally, the responses of the high-rise building are investigated using the Periodogram method to conclude that the identified results agree well with the results computed by the Finite Element Method (FEM).

Designing strategy for multi-agent system based large structural health monitoring

Density and different kinds of sensors have to be adopted to have a reliable monitoring of large scale engineering structures. To deal with the diverse, heterogeneous and distributed information obtained by these sensors at different sites on the structure, multi-agent system (MAS) based structural health monitoring (SHM) technology is researched in detail in this paper. A general framework to design the multi-agent system for large scale structural health monitoring is proposed. Compared to past work, this framework is typical and flexible, which is easy to be referenced by other researchers to develop their own multi-agent based SHM system. The design strategy of the MAS based SHM is discussed in detail, including function design, ontology design, individual agent design, facilitator design, society behavior and learning behavior design. A MAS based composite SHM system is researched as a case study to evaluate the efficiency of the proposed MAS based SHM system design strategy and the advantages of the MAS based SHM system. Very detail introduction of the implementation of this system is given in the paper.

Wireless Transmission for Health Monitoring of Large Structures

In order to monitor the health of large structures such as highways, bridges, buildings, and dams, the outputs from various kinds of sensors, which are positioned at different places of structures, should be collected simultaneously and/or sequentially. However, the placements of sensors and wire connections are time consuming and cost expensive. Furthermore, the wires are easy to be damaged during the placement operation or in service. In this paper, the configurations of the wireless health-monitoring systems for structures are discussed. A novel wireless signal retrieval system, which is suitable for transmitting wirelessly the response signals of large structures, is proposed and developed. Special attention is paid to the low-frequency characteristics of the wireless transmitter/receiver pairs due to the following two reasons: The response frequencies of large structures are very low; on the other hand, the commercially available transmitter/receiver pairs have poor characteristics at low frequencies. The principles, feasibility, and configuration of the wireless-transmission system for the health monitoring of large structures are investigated. Also, experiments for testing the system performances are performed in the laboratory. The results show that the proposed wireless-transmission method and the developed system are suitable for the in situ and in-service wireless health monitoring of large structures.

A carbon nanotube strain sensor for structural health monitoring

A carbon nanotube polymer material was used to form a piezoresistive strain sensor forstructural health monitoring applications. The polymer improves the interfacial bondingbetween the nanotubes. Previous single walled carbon nanotube buckypaper sensorsproduced distorted strain measurements because the van der Waals attraction force allowedaxial slipping of the smooth surfaces of the nanotubes. The polymer sensor uses largermulti-walled carbon nanotubes which improve the strain transfer, repeatability andlinearity of the sensor. An electrical model of the nanotube strain sensor was derived basedon electrochemical impedance spectroscopy and strain testing. The model is useful fordesigning nanotube sensor systems. A biomimetic artificial neuron was developedby extending the length of the sensor. The neuron is a long continuous strainsensor that has a low cost, is simple to install and is lightweight. The neuron has alow bandwidth and adequate strain sensitivity. The neuron sensor is particularlyuseful for detecting large strains and cracking, and can reduce the number ofchannels of data acquisition needed for the health monitoring of large structures.

Spatial-frequency multiplexed fiber-optic Fizeau strain sensor system with optical amplification

A novel method for multiplexing fiber-optic Fizeau strain sensors with optical amplification is proposed and demonstrated. This method overcomes the two intrinsic disadvantages of fiber-optic Fabry–Perot (F–P) strain sensors, i.e. weak signal and difficult multiplexing. The amplified spontaneous emission (ASE) and optical amplification are used simultaneously to enhance the interferometric signal considerably. A Fizeau interferometer formed by two fiber ends with a quite different reflectivity is used to replace the F–P cavity in sensor head design. Such a Fizeau cavity can enlarge the cavity length by at least an order of magnitude and allows more than 10 sensors to be multiplexed simultaneously by using spatial-frequency multiplexing. The operating principle of the sensor system is discussed and an experiment is carried out to verify the concept of the method proposed. It is anticipated that such a sensor system could find important applications for health monitoring of large structures.