Showing papers on "Structural health monitoring published in 2015"

Fibre Optic Sensors for Structural Health Monitoring of Aircraft Composite Structures: Recent Advances and Applications.

Abstract: In-service structural health monitoring of composite aircraft structures plays a key role in the assessment of their performance and integrity. In recent years, Fibre Optic Sensors (FOS) have proved to be a potentially excellent technique for real-time in-situ monitoring of these structures due to their numerous advantages, such as immunity to electromagnetic interference, small size, light weight, durability, and high bandwidth, which allows a great number of sensors to operate in the same system, and the possibility to be integrated within the material. However, more effort is still needed to bring the technology to a fully mature readiness level. In this paper, recent research and applications in structural health monitoring of composite aircraft structures using FOS have been critically reviewed, considering both the multi-point and distributed sensing techniques.

Flexible and Highly Sensitive Strain Sensors Fabricated by Pencil Drawn for Wearable Monitor

Abstract: Functional electrical devices have promising potentials in structural health monitoring system, human-friendly wearable interactive system, smart robotics, and even future multifunctional intelligent room. Here, a low-cost fabrication strategy to efficiently construct highly sensitive graphite-based strain sensors by pencil-trace drawn on flexible printing papers is reported. The strain sensors can be operated at only two batteries voltage of 3 V, and can be applied to variously monitoring microstructural changes and human motions with fast response/relaxation times of 110 ms, a high gauge factor (GF) of 536.6, and high stability >10 000 bending–unbending cycles. Through investigation of service behaviors of the sensors, it is found that the microcracks occur on the surface of the pencil-trace and have a major influence on the functions of the strain sensors. These performances of the strain sensor attain and even surpass the properties of recent strain sensing devices with subtle design of materials and device architectures. The pen-on-paper (PoP) approach may further develop portable, environmentally friendly, and economical lab-on-paper applications and offer a valuable method to fabricate other multifunctional devices.

A Vision-Based Sensor for Noncontact Structural Displacement Measurement

Abstract: Conventional displacement sensors have limitations in practical applications This paper develops a vision sensor system for remote measurement of structural displacements An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images By simply adjusting the upsampling factor, better subpixel resolution can be easily achieved to improve the measurement accuracy The performance of the vision sensor is first evaluated through a laboratory shaking table test of a frame structure, in which the displacements at all the floors are measured by using one camera to track either high-contrast artificial targets or low-contrast natural targets on the structural surface such as bolts and nuts Satisfactory agreements are observed between the displacements measured by the single camera and those measured by high-performance laser displacement sensors Then field tests are carried out on a railway bridge and a pedestrian bridge, through which the accuracy of the vision sensor in both time and frequency domains is further confirmed in realistic field environments Significant advantages of the noncontact vision sensor include its low cost, ease of operation, and flexibility to extract structural displacement at any point from a single measurement

Dynamic Characterization of a Soft Elastomeric Capacitor for Structural Health Monitoring

Abstract: Structural health monitoring of civil infrastructures is a difficult task, often impeded by the geometrical size of the monitored systems. Recent advances in conducting polymers enabled the fabrication of flexible sensors capable of covering large areas, a possible solution to the monitoring challenge of mesoscale systems. The authors have previously proposed a novel sensor consisting of a soft elastomeric capacitor (SEC) acting as a strain gauge. Arranged in a network configuration, the SECs have the potential to cover very large surfaces. In this paper, understanding of the proposed sensor is furthered by evaluating its performance at vibration-based monitoring of large-scale structures. The dynamic behavior of the SEC is characterized by subjecting the sensor to a frequency sweep, and detecting vibration modes of a full-scale steel beam. Results show that the sensor can be used to detect fundamental modes and dynamic input. Also, a network of SECs is used for output-only modal identification of...

Damage classification in carbon fibre composites using acoustic emission: A comparison of three techniques

Abstract: Classifying the type of damage occurring within a structure using a structural health monitoring system can allow the end user to assess what kind of repairs, if any, that a component requires. This paper investigates the use of acoustic emission (AE) to locate and classify the type of damage occurring in a composite, carbon fibre panel during buckling. The damage was first located using a bespoke location algorithm developed at Cardiff University, called delta-T mapping. Signals identified as coming from the regions of damage were then analysed using three AE classification techniques; Artificial Neural Network (ANN) analysis, Unsupervised Waveform Clustering (UWC) and corrected Measured Amplitude Ratio (MAR). A comparison of results yielded by these techniques shows a strong agreement regarding the nature of the damage present in the panel, with the signals assigned to two different damage mechanisms, believed to be delamination and matrix cracking. Ultrasonic C-scan images and a digital image correlation (DIC) analysis of the buckled panel were used as validation. MAR’s ability to reveal the orientation of recorded signals greatly assisted the identification of the delamination region, however, ANN and UWC have the ability to group signals into several different classes, which would prove useful in instances where several damage mechanisms were generated. Combining each technique’s individual merits in a multi-technique analysis dramatically improved the reliability of the AE investigation and it is thought that this cross-correlation between techniques will also be the key to developing a reliable SHM system.

Deploying Wireless Sensor Networks with Fault-Tolerance for Structural Health Monitoring

Abstract: Structural health monitoring (SHM) systems are implemented for structures (e.g., bridges, buildings) to monitor their operations and health status. Wireless sensor networks (WSNs) are becoming an enabling technology for SHM applications that are more prevalent and more easily deployable than traditional wired networks. However, SHM brings new challenges to WSNs: engineering-driven optimal deployment, a large volume of data, sophisticated computing, and so forth. In this paper, we address two important challenges: sensor deployment and decentralized computing. We propose a solution, to deploy wireless sensors at strategic locations to achieve the best estimates of structural health (e.g., damage) by following the widely used wired sensor system deployment approach from civil/structural engineering. We found that faults (caused by communication errors, unstable connectivity, sensor faults, etc.) in such a deployed WSN greatly affect the performance of SHM. To make the WSN resilient to the faults, we present an approach, called ${\tt FTSHM}$ (fault-tolerance in SHM), to repair the WSN and guarantee a specified degree of fault tolerance. ${\tt FTSHM}$ searches the repairing points in clusters in a distributed manner, and places a set of backup sensors at those points in such a way that still satisfies the engineering requirements. ${\tt FTSHM}$ also includes an SHM algorithm suitable for decentralized computing in the energy-constrained WSN, with the objective of guaranteeing that the WSN for SHM remains connected in the event of a fault, thus prolonging the WSN lifetime under connectivity and data delivery constraints. We demonstrate the advantages of ${\tt FTSHM}$ through extensive simulations and real experimental settings on a physical structure.

Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization

Abstract: Modal strain energy (MSE) is a sensitive physical property that can be utilized as a damage index in structural health monitoring. Inverse problem solving-based approaches using single-objective optimization algorithms are also a promising damage identification method. However, the research into the integration of these methods is currently limited; only partial success in the detection of structural damage with high errors has been reported. The majority of previous research was focused on detecting damage in simply supported beams or plain structures. In this study, a novel damage detection approach using hybrid multiobjective optimization algorithms based on MSE is proposed to detect damages in various three-dimensional (3-D) steel structures. Minor damages have little effect on the difference of the modal properties of the structure, and thus such damages with multiple locations in a structure are difficult to detect using traditional damage detection methods based on modal properties. Various minor damage scenarios are created for the 3-D structures to investigate the newly proposed multiobjective approach. The proposed hybrid multiobjective genetic algorithm detects the exact locations and extents of the induced minor damages in the structure. Even though it uses incomplete mode shapes, which do not have any measured information at the damaged element, the proposed approach detects damage well. The robustness of the proposed method is investigated by adding 5% Gaussian random white noise as a noise effect to mode shapes, which are used in the calculation ofMSE.

Optical Fiber Sensors for Aircraft Structural Health Monitoring

Abstract: Aircraft structures require periodic and scheduled inspection and maintenance operations due to their special operating conditions and the principles of design employed to develop them. Therefore, structural health monitoring has a great potential to reduce the costs related to these operations. Optical fiber sensors applied to the monitoring of aircraft structures provide some advantages over traditional sensors. Several practical applications for structures and engines we have been working on are reported in this article. Fiber Bragg gratings have been analyzed in detail, because they have proved to constitute the most promising technology in this field, and two different alternatives for strain measurements are also described. With regard to engine condition evaluation, we present some results obtained with a reflected intensity-modulated optical fiber sensor for tip clearance and tip timing measurements in a turbine assembled in a wind tunnel.

The use of carbon nanotubes for damage sensing and structural health monitoring in laminated composites: a review

Abstract: The increasing use of fiber-reinforced plastics (FRPs) in industries such as aerospace, marine, and automotive, has resulted in a necessity to monitor the structural integrity of composite structures and materials. Apart from development of traditional non-destructive testing methods which are performed off-line, there is a growing need to integrate structural health monitoring (SHM) systems within composite structures. An interesting route toward multifunctional composite materials with integrated SHM capabilities is through the introduction of carbon nanotubes (CNTs) in fiber-reinforced composites as this provides not only integrated damage sensing capability, but may, at the same time, also lead to some additional mechanical reinforcement. Since the first use of CNTs for damage sensing in composite laminates, a significant number of studies have dealt with this topic, but a systematic understanding on the use of CNTs in FRPs for SHM is still lacking. Furthermore, a significant gap remains betwe...

Structural Health Monitoring of Aerospace Composites

Abstract: Structural Health Monitoring of Aerospace Composite Structures offers a comprehensive review of established and promising technologies under development in the emerging area of structural health monitoring (SHM) of aerospace composite structures Beginning with a description of the different types of composite damage, which differ fundamentally from the damage states encountered in metallic airframes, the book moves on to describe the SHM methods and sensors currently under consideration before considering application examples related to specific composites, SHM sensors, and detection methods Expert author Victor Giurgiutiu closes with a valuable discussion of the advantages and limitations of various sensors and methods, helping you to make informed choices in your structure research and developmentThe first comprehensive review of one of the most ardent research areas in aerospace structures, providing breadth and detail to bring engineers and researchers up to speed on this rapidly developing fieldCovers the main classes of SHM sensors, including fiber optic sensors, piezoelectric wafer active sensors, electrical properties sensors and conventional resistance strain gauges, and considers their applications and limitationIncludes details of active approaches, including acousto-ultrasonics, vibration, frequency transfer function, guided-wave tomography, phased arrays, and electrochemical impedance spectroscopy (ECIS), among other emerging methods

Simulation Methods for Guided Wave-Based Structural Health Monitoring: A Review

Abstract: This paper reviews the state-of-the-art in numerical wave propagation analysis. The main focus in that regard is on guided wave-based structural health monitoring (SHM) applications. A brief introduction to SHM and SHM-related problems is given, and various numerical methods are then discussed and assessed with respect to their capability of simulating guided wave propagation phenomena. A detailed evaluation of the following methods is compiled: (i) analytical methods, (ii) semi-analytical methods, (iii) the local interaction simulation approach (LISA), (iv) finite element methods (FEMs), and (v) miscellaneous methods such as mass–spring lattice models (MSLMs), boundary element methods (BEMs), and fictitious domain methods. In the framework of the FEM, both time and frequency domain approaches are covered, and the advantages of using high order shape functions are also examined.

Nontarget Vision Sensor for Remote Measurement of Bridge Dynamic Response

Abstract: Displacements of railroad bridges under trainloads need to be closely monitored, but conventional displacement sensors have limitations for use in the field. This paper presents a new vision-based sensor system developed for remote measurement of structural dynamic displacements without requiring a specially installed target-marker panel. By implementing a robust object-search algorithm, the displacement can be accurately measured by tracking existing bridge surface features from a remote distance. The accuracy of measured dynamic displacements was first evaluated using a shaking table test. Then field tests were carried out on two railroad bridges subjected to freight trainloads traveling at various speeds. Measurements were taken remotely during the daytime and also at night from different distances with and without a target panel. Through comparison with a conventional contact-type displacement sensor, the high accuracy of the proposed nontarget remote-sensor system was demonstrated in the real...

Image-Based Process Monitoring Using Low-Rank Tensor Decomposition

Abstract: Image and video sensors are increasingly being deployed in complex systems due to the rich process information that these sensors can capture. As a result, image data play an important role in process monitoring and control in different application domains such as manufacturing processes, food industries, medical decision-making, and structural health monitoring. Existing process monitoring techniques fail to fully utilize the information of color images due to their complex data characteristics including the high-dimensionality and correlation structure (i.e., temporal, spatial and spectral correlation). This paper proposes a new image-based process monitoring approach that is capable of handling both grayscale and color images. The proposed approach models the high-dimensional structure of the image data with tensors and employs low-rank tensor decomposition techniques to extract important monitoring features monitored using multivariate control charts. In addition, this paper shows the analytical relationships between different low-rank tensor decomposition methods. The performance of the proposed method in quick detection of process changes is evaluated and compared with existing methods through extensive simulations and a case study in a steel tube manufacturing process.

Citizen Sensors for SHM: Use of Accelerometer Data from Smartphones

Abstract: Ubiquitous smartphones have created a significant opportunity to form a low-cost wireless Citizen Sensor network and produce big data for monitoring structural integrity and safety under operational and extreme loads. Such data are particularly useful for rapid assessment of structural damage in a large urban setting after a major event such as an earthquake. This study explores the utilization of smartphone accelerometers for measuring structural vibration, from which structural health and post-event damage can be diagnosed. Widely available smartphones are tested under sinusoidal wave excitations with frequencies in the range relevant to civil engineering structures. Large-scale seismic shaking table tests, observing input ground motion and response of a structural model, are carried out to evaluate the accuracy of smartphone accelerometers under operational, white-noise and earthquake excitations of different intensity. Finally, the smartphone accelerometers are tested on a dynamically loaded bridge. The extensive experiments show satisfactory agreements between the reference and smartphone sensor measurements in both time and frequency domains, demonstrating the capability of the smartphone sensors to measure structural responses ranging from low-amplitude ambient vibration to high-amplitude seismic response. Encouraged by the results of this study, the authors are developing a citizen-engaging and data-analytics crowdsourcing platform towards a smartphone-based Citizen Sensor network for structural health monitoring and post-event damage assessment applications.

Damage identification scheme based on compressive sensing

Abstract: Civil infrastructures are critical to every nation, due to their substantial investment, long service period, and enormous negative impacts after failure. However, they inevitably deteriorate during their service lives. Therefore, methods capable of assessing conditions and identifying damage in a structure timely and accurately have drawn increasing attention. Recently, compressive sensing (CS), a significant breakthrough in signal processing, has been proposed to capture and represent compressible signals at a rate significantly below the traditional Nyquist rate. Due to its sound theoretical background and notable influence, this methodology has been successfully applied in many research areas. In order to explore its application in structural damage identification, a new CS-based damage identification scheme is proposed in this paper, by regarding damage identification problems as pattern classification problems. The time domain structural responses are transferred to the frequency domain as sparse representation, and then the numerical simulated data under various damage scenarios will be used to train a feature matrix as input information. This matrix can be used for damage identification through an optimization process. This will be one of the first few applications of this advanced technique to structural engineering areas. In order to demonstrate its effectiveness, numerical simulation results on a complex pipe soil interaction model are used to train the parameters and then to identify the simulated pipe degradation damage and free-spanning damage. To further demonstrate the method, vibration tests of a steel pipe laid on the ground are carried out. The measured acceleration time histories are used for damage identification. Both numerical and experimental verification results confirm that the proposed damage identification scheme will be a promising tool for structural health monitoring.

Optimal sensor placement in structural health monitoring using discrete optimization

Abstract: The objective of optimal sensor placement (OSP) is to obtain a sensor layout that gives as much information of the dynamic system as possible in structural health monitoring (SHM). The process of OSP can be formulated as a discrete minimization (or maximization) problem with the sensor locations as the design variables, conditional on the constraint of a given sensor number. In this paper, we propose a discrete optimization scheme based on the artificial bee colony algorithm to solve the OSP problem after first transforming it into an integer optimization problem. A modal assurance criterion-oriented objective function is investigated to measure the utility of a sensor configuration in the optimization process based on the modal characteristics of a reduced order model. The reduced order model is obtained using an iterated improved reduced system technique. The constraint is handled by a penalty term added to the objective function. Three examples, including a 27 bar truss bridge, a 21-storey building at the MIT campus and the 610 m high Canton Tower, are investigated to test the applicability of the proposed algorithm to OSP. In addition, the proposed OSP algorithm is experimentally validated on a physical laboratory structure which is a three-story two-bay steel frame instrumented with triaxial accelerometers. Results indicate that the proposed method is efficient and can be potentially used in OSP in practical SHM.

Structural health monitoring (SHM) of aerospace composites

Abstract: The chapter starts with a discussion of the structural health monitoring (SHM) techniques for aerospace composites. This is followed by a presentation of the major sensor classes used in SHM practice with focus on advanced sensors such as optical fiber Bragg gratings (FBG) and piezoelectric wafer active sensors (PWAS). Electrical sensing methods for composites SHM are also discussed. The chapter expands the discussion on several tracks such as passive sensing SHM, active sensing SHM, local-area sensing with the electromechanical impedance spectroscopy (EMIS), active sensing SHM with electrical methods, direct methods for impact damage detection. The chapter finishes with summary, conclusions, and suggestions for further work.

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

Abstract: Lossy transmission is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data; the promise of this approach is to reduce communication and thus power savings. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. Specifically, this paper targets to provide accurate compensation for stationary and compressible acceleration signals obtained from structural health monitoring (SHM) systems with data loss ratio below 20%. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

Detection of steel fatigue cracks with strain sensing sheets based on large area electronics.

Abstract: Reliable early-stage damage detection requires continuous monitoring over large areas of structure, and with sensors of high spatial resolution. Technologies based on Large Area Electronics (LAE) can enable direct sensing and can be scaled to the level required for Structural Health Monitoring (SHM) of civil structures and infrastructure. Sensing sheets based on LAE contain dense arrangements of thin-film strain sensors, associated electronics and various control circuits deposited and integrated on a flexible polyimide substrate that can cover large areas of structures. This paper presents the development stage of a prototype strain sensing sheet based on LAE for crack detection and localization. Two types of sensing-sheet arrangements with size 6 × 6 inch (152 × 152 mm) were designed and manufactured, one with a very dense arrangement of sensors and the other with a less dense arrangement of sensors. The sensing sheets were bonded to steel plates, which had a notch on the boundary, so the fatigue cracks could be generated under cyclic loading. The sensors within the sensing sheet that were close to the notch tip successfully detected the initialization of fatigue crack and localized the damage on the plate. The sensors that were away from the crack successfully detected the propagation of fatigue cracks based on the time history of the measured strain. The results of the tests have validated the general principles of the proposed sensing sheets for crack detection and identified advantages and challenges of the two tested designs.

Wireless Passive RFID Crack Width Sensor for Structural Health Monitoring

Abstract: All mechanical structures are subjected to deformation and cracks, due to fatigue, stress, and/or environmental factors. It is, therefore, of uttermost importance to monitor the mechanical condition of critical structures, in order to prevent catastrophic failures, but also to minimize maintenance costs, i.e., avoid unnecessary inspections. A number of technologies and systems can be used for this purpose: among them, the ones proposing the use of wireless passive crackmeters have a strong impact potential, in terms of simplicity of installation and measurement and low cost. This paper, hence, shows a crack width wireless radio-frequency identification sensor, developed for applications on various materials (such as concrete and metal) and able to detect submillimeter deformations occurring on the object, on which it is placed. A design method based on high-sensitivity phase detection is shown.

Categories of SHM Deployments: Technologies and Capabilities

Abstract: The findings of an extensive literature survey focusing on bridge structural health monitoring (SHM) deployments are presented. Conventional, maturing, and emerging technologies are reviewed as well as deployment considerations for new SHM endeavors. The lack of published calibration studies (and quantification of uncertainty studies) for new sensors is highlighted as a major concern and area for future research. There are currently very few examples of SHM systems that have clearly provided significant value to the owners of monitored structures. The results of the literature survey are used to propose a categorization system to better assess the potential outcomes of bridge SHM deployments. It is shown that SHM studies can be categorized as one (or a combination) of the following: (1) anomaly detection, (2) sensor deployment studies, (3) model validation, (4) threshold check, and (5) damage detection. The new framework aids engineers specifying monitoring systems to determine what should be measured and why, hence allowing them to better evaluate what value may be delivered to the relevant stakeholders for the monitoring investments.

Review of Trackside Monitoring Solutions: From Strain Gages to Optical Fibre Sensors.

Abstract: A review of recent research on structural monitoring in railway industry is proposed in this paper, with a special focus on stress-based solutions. After a brief analysis of the mechanical behaviour of ballasted railway tracks, an overview of the most common monitoring techniques is presented. A special attention is paid on strain gages and accelerometers for which the accurate mounting position on the track is requisite. These types of solution are then compared to another modern approach based on the use of optical fibres. Besides, an in-depth discussion is made on the evolution of numerical models that investigate the interaction between railway vehicles and tracks. These models are used to validate experimental devices and to predict the best location(s) of the sensors. It is hoped that this review article will stimulate further research activities in this continuously expanding field.