Showing papers on "Structural health monitoring published in 2006"

A summary review of wireless sensors and sensor networks for structural health monitoring

Abstract: In recent years, there has been an increasing interest in the adoption of emerging sensing technologies for instrumentation within a variety of structural systems. Wireless sensors and sensor networks are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered monitoring systems. A benefit of wireless structural monitoring systems is that they are inexpensive to install because extensive wiring is no longer required between sensors and the data acquisition system. Researchers are discovering that wireless sensors are an exciting technology that should not be viewed as simply a substitute for traditional tethered monitoring systems. Rather, wireless sensors can play greater roles in the processing of structural response data; this feature can be utilized to screen data for signs of structural damage. Also, wireless sensors have limitations that require novel system architectures and modes of operation. This paper is intended to serve as a summary review of the collective experience the structural engineering community has gained from the use of wireless sensors and sensor networks for monitoring structural performance and health.

A review of vibration-based structural health monitoring with special emphasis on composite materials

Abstract: Structural health monitoring and damage detection techniques are tools of great importance in the off-shore, civil, mechanical and aeronautical engineering communities, both for safety reasons and because of the economic benefits that can result. The need to be able to detect damage in complex structures has led to the development of a vast range of techniques, of which many are based upon structural vibration analysis. In the present article, some of the latest advances in Structural Health Monitoring and Damage Detection are reviewed, with an emphasis on composite structures on the grounds that this class of materials currently has a wide range of engineering applications. FOREWORD-It should be noted that this review is not intended to be a general, all-encompassing review covering the whole range of structural health monitoring (SHM); it was planned as the starting point for a study focusing on damage detection, localization and assessment for certain kinds of structure. Thus, the line of thought behind the search and the structure of this review is a result of objectives beyond the scope of the paper itself. Nevertheless, it was considered that, once the above was understood, an updated synopsis such as this could also be useful for other researchers in the same field. ©2006 SAGE Publications.

Fiber Bragg grating sensors for structural health monitoring of Tsing Ma bridge : Background and experimental observation

Abstract: The rapid expansion of the optical fiber telecommunication industry due to the explosion of the Internet has substantially driven down the cost of optical components, making fiber optic sensors more economically viable. In addition, the rapid development of fiber-optic sensors, particularly the fiber Bragg grating (FBG) sensors offers many advantages and capability that could not be achieved otherwise. In the past few years, fiber Bragg grating sensors have attracted a lot of interest and they are being used in numerous applications. This paper describes the FBG sensors developed for structural health monitoring, and were installed on Hong Kong's landmark Tsing Ma bridge (TMB), which is the world longest (1377 m) suspension bridge that carried both railway and regular road traffic. Forty FBG sensors divided into three arrays were installed on the hanger cable, rocker bearing and truss girders of the TMB. The objectives of the study are to investigate the feasibility of using the developed FBG sensors for structural health monitoring, via monitoring the strain of different parts of the TMB under both the railway and highway loads as well as comparing the FBG sensors' performance with the conventional structural health monitoring system - Wind and Structural Health Monitoring System (WASHMS) that has been operating at TMB since the bridge's commissioning in May 1997. The experimental observations in this project show that the results using FBG sensors were in excellent agreement with those acquired by WASHMS.

Wavelet Transform for Structural Health Monitoring: A Compendium of Uses and Features:

Abstract: The strategic and monetary value of the civil infrastructure worldwide necessitates the development of structural health monitoring (SHM) systems that can accurately monitor structural response due to real-time loading conditions, detect damage in the structure, and report the location and nature of this damage. In the last decade, extensive research has been carried out for developing vibration-based damage detection algorithms that can relate structural dynamics changes to damage occurrence in a structure. In the mean time, the wavelet transform (WT), a signal processing technique based on a windowing approach of dilated ‘scaled’ and shifted wavelets, is being applied to a broad range of engineering applications. Wavelet transform has proven its ability to overcome many of the limitations of the widely used Fourier transform (FT); hence, it has gained popularity as an efficient means of signal processing in SHM systems. This increasing interest in WT for SHM in diverse applications motivates the authors...

Monitoring civil structures with a wireless sensor network

Abstract: Structural health monitoring (SHM) is an active area of research devoted to systems that can autonomously and proactively assess the structural integrity of bridges, buildings, and aerospace vehicles. Recent technological advances promise the eventual ability to cover a large civil structure with low-cost wireless sensors that can continuously monitor a building's structural health, but researchers face several obstacles to reaching this goal, including high data-rate, data-fidelity, and time-synchronization requirements. This article describes two systems the authors recently deployed in real-world structures.

Performance assessment and validation of piezoelectric active-sensors in structural health monitoring

Abstract: A sensor diagnostics and validation process that performs in situ monitoring of the operational status of piezoelectric (PZT) active-sensors in structural health monitoring (SHM) applications is presented. Both degradation of the mechanical/electrical properties of a PZT transducer and the bonding defects between a PZT patch and a host structure could be identified by the proposed process. This study also includes the investigation into the effects of the sensor/structure bonding defects on high-frequency SHM techniques, including Lamb wave propagations and impedance methods. It has been found that the effects are significant, modifying the phase and amplitude of propagated waves and changing the measured impedance spectrum. These changes could lead to false indications on the structural conditions without an efficient sensor-diagnostic process. The feasibility of the proposed sensor diagnostics procedure is then demonstrated by analytical studies and experimental examples, where the functionality of the surface-mounted piezoelectric sensors was continuously deteriorated. The proposed process can provide a metric that can be used to determine the sensor functionality over a long period of service time or after an extreme loading event. Further, the proposed method can be useful if one needs to check the operational status of a sensing network right after its installation.

Multiple Crack Detection of Concrete Structures Using Impedance-based Structural Health Monitoring Techniques

Abstract: This paper presents a feasibility study for practical applications of an impedance-based real-time health monitoring technique applying PZT (Lead–Zirconate–Titanate) patches to concrete structures. First, comparison between experimental and analytical studies for damage detection on a plain concrete beam is made. In the experimental study, progressive surface damage inflicted artificially on the plain concrete beam is assessed by using both lateral and thickness modes of the PZT patches. Then, an analytical study based on finite element (FE) models is carried out to verify the validity of the experimental result. Secondly, multiple (shear and flexural) cracks incurred in a reinforced concrete (RC) beam under a third point bending test are monitored continuously by using a sensor array system composed of the PZT patches. In this study, a root mean square deviation (RMSD) in the impedance signatures of the PZT patches is used as a damage indicator.

The temperature stability of guided wave structural health monitoring systems

Abstract: It is desirable for any structural health monitoring (SHM) system to have maximum sensitivity with minimum sensor density. The structural health monitoring system described here is based on the excitation and reception of guided waves using piezoelectric elements as sensors. One of the main challenges faced is that in all but the most simple structures the wave interactions become too complex for the time domain signals to be interpreted directly. One approach to overcoming this complexity is to subtract a baseline reference signal from the measured system when it is known to be defect free. This strategy enables changes in the structure to be identified. Two key issues must be addressed to allow this paradigm to become a reality. First, the system must be sufficiently sensitive to small reflections from defects such as cracking. Second, it must be able to distinguish between benign changes and those due to structural defects. In this paper the baseline subtraction approach is used to detect defects in a simple rectangular plate. The system is shown to work well in the short term, and good sensitivity to defects is demonstrated. The performance degrades over the medium to long term. The principal reason for this degradation is shown to be the effect of change in temperature of the system. These effects are quantified and strategies for overcoming them are discussed.

Application of time series analysis for bridge monitoring

Abstract: Despite recent considerable advances in structural health monitoring (SHM) of civil infrastructure, converting large amounts of data from SHM systems into usable information and knowledge remains a great challenge. This paper addresses the problem through the analysis of time histories of static strain data recorded by an SHM system installed in a major bridge structure and operating continuously for a long time. The reported study formulates a vector seasonal autoregressive integrated moving average (ARIMA) model for the recorded strain signals. The coefficients of the ARIMA model are allowed to vary with time and are identified using an adaptive Kalman filter. The proposed method has been used for analysis of the signals recorded during the construction and service life of the bridge. By observing various changes in the ARIMA model coefficients, unusual events as well as structural change or damage sustained by the structure can be revealed.

Piezoelectric Active Sensor Self-Diagnostics Using Electrical Admittance Measurements

Abstract: This paper presents a piezoelectric sensor self-diagnostic procedure that performs in situ monitoring of the operational status of piezoelectric materials used for sensors and actuators in structural health monitoring (SHM) applications. The sensor/actuator self-diagnostic procedure, where the sensors/actuators are confirmed to be functioning properly during operation, is a critical component to successfully complete the SHM process with large numbers of active sensors typically installed in a structure. The premise of this procedure is to track the changes in the capacitive value of piezoelectric materials resulting from the degradation of the mechanical/electrical properties and its attachment to a host structure, which is manifested in the imaginary part of the measured electrical admittances. This paper concludes with an experimental example to demonstrate the feasibility of the proposed procedure.

Efficient model updating and health monitoring methodology using incomplete modal data without mode matching

Abstract: A methodology is presented for Bayesian structural model updating using noisy incomplete modal data corresponding to natural frequencies and partial mode shapes of some of the modes of a structural system. The procedure can be used to find the most probable model within a specified class of structural models, based on the incomplete modal data, as well as the most probable values of the system natural frequencies and the full system mode shapes. The method does not require matching measured modes with corresponding modes from the structural model, which is in contrast to many existing methods. To find the most probable values of the structural model parameters and system modal parameters, the method uses an iterative scheme involving a series of coupled linear optimization problems. Furthermore, it does not require solving the eigenvalue problem of any structural model; instead, the eigenvalue equations appear in the prior probability distribution to provide soft constraints. The method appears to be computationally efficient and robust, judging from its successful application to noisy simulated data for a ten-storey building model and for a three-dimensional braced-frame model. This latter example is also used to demonstrate an application to structural health monitoring.

Health monitoring and rehabilitation of a concrete structure using intelligent materials

Abstract: This paper presents the concept of an intelligent reinforced concrete structure (IRCS) and its application in structural health monitoring and rehabilitation. The IRCS has multiple functions which include self-rehabilitation, self-vibration damping, and self-structural health monitoring. These functions are enabled by two types of intelligent (smart) materials: shape memory alloys (SMAs) and piezoceramics. In this research, Nitinol type SMA and PZT (lead zirconate titanate) type piezoceramics are used. The proposed concrete structure is reinforced by martensite Nitinol cables using the method of post-tensioning. The martensite SMA significantly increases the concrete's damping property and its ability to handle large impact. In the presence of cracks due to explosions or earthquakes, by electrically heating the SMA cables, the SMA cables contract and close up the cracks. In this research, PZT patches are embedded in the concrete structure to detect possible cracks inside the concrete structure. The wavelet packet analysis method is then applied as a signal-processing tool to analyze the sensor signals. A damage index is defined to describe the damage severity for health monitoring purposes. In addition, by monitoring the electric resistance change of the SMA cables, the crack width can be estimated. To demonstrate this concept, a concrete beam specimen with reinforced SMA cables and with embedded PZT patches is fabricated. Experiments demonstrate that the IRC has the ability of self-sensing and self-rehabilitation. Three-point bending tests were conducted. During the loading process, a crack opens up to 0.47 inches. Upon removal of the load and heating the SMA cables, the crack closes up. The damage index formed by wavelet packet analysis of the PZT sensor data predicts and confirms the onset and severity of the crack during the loading. Also during the loading, the electrical resistance value of the SMA cable changes by up to 27% and this phenomenon is used to monitor the crack width.

Detection of bolt loosening in C–C composite thermal protection panels: I. Diagnostic principle

Abstract: A concept demonstrator of the structural health monitoring (SHM) system was developed to autonomously detect the degradation of the mechanical integrity of the standoff carbon–carbon (C–C) thermal protection system (TPS) panels. This system enables us to identify the location of the loosened bolts, as well as to predict the torque levels of those bolts accordingly. In the process of building the proposed SHM prototype, efforts have been focused primarily on developing a trustworthy diagnostic scheme and a responsive sensor suite. In part I of the study, an attenuation-based diagnostic method was proposed to assess the fastener integrity by observing the attenuation patterns of the resultant sensor signals. The attenuation-based method is based on the damping phenomena of ultrasonic waves across the bolted joints. The major advantage of the attenuation-based method over the conventional diagnostic methods is its local sensing capability of loosened brackets. The method can further discriminate the two major failure modes within a bracket: panel-joint loosening and bracket-joint loosening. The theoretical explanation of the attenuation-based method is performed using micro-contact theory and structural/internal damping principles, followed by parametric model studies and appropriate hypothesis testing.

Design and characterization of a passive wireless strain sensor

Abstract: A novel passive wireless-interrogation strain sensor is presented in this paper. The sensor employs a planar inductor with a series connected interdigital capacitor to eliminate the wire connection for power supply and data transmission. The sensor is activated by electromagnetic waves and the resonant frequency of the sensor is interrogated remotely with a single loop antenna by applying an oscillating signal to the antenna and monitoring the frequency response of the voltage across it. The prototype sensor and reader were designed and fabricated. The results of calibration on a constant-strain cantilever beam show great linearity and sensitivity. The innovative wireless strain sensing technology described herein has demonstrated a great potential to extend its applications in structural health monitoring, damage detection, condition-based maintenance, failure prevention and non-destructive evaluation.

Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler

Abstract: A new Bayesian model updating approach is presented for linear structural models. It is based on the Gibbs sampler, a stochastic simulation method that decomposes the uncertain model parameters into three groups, so that the direct sampling from any one group is possible when conditional on the other groups and the incomplete modal data. This means that even if the number of uncertain parameters is large, the effective dimension for the Gibbs sampler is always three and so high-dimensional parameter spaces that are fatal to most sampling techniques are handled by the method, making it more practical for health monitoring of real structures. The approach also inherits the advantages of Bayesian techniques: it not only updates the optimal estimate of the structural parameters but also updates the associated uncertainties. The approach is illustrated by applying it to two examples of structural health monitoring problems, in which the goal is to detect and quantify any damage using incomplete modal data obtained from small-amplitude vibrations measured before and after a severe loading event, such as an earthquake or explosion.

Performance of Vibration-based Techniques for the Identification of Structural Damage:

Abstract: Early detection of damage is of special concern for civil engineering structures. If not identified in time, damage may have serious consequences, both safety related and economic. The traditional methods of damage detection include visual inspection or instrumental evaluation. A comparatively recent development in the health monitoring of civil engineering structures is vibration-based damage detection. Vibration characteristics of a structure, that is, its frequencies, mode shapes, and damping are directly affected by the physical characteristics of the structure including its mass and stiffness. Damage reduces the stiffness of the structure and alters its vibration characteristics. Therefore, measurement and monitoring of vibration characteristics should theoretically permit the detection of both the location and severity of damage. However, in practice, a number of difficulties persist in vibration-based damage identification. As a result, most of the damage identification algorithms fail when applied...

Modeling and testing of PZT and PVDF piezoelectric wafer active sensors

Abstract: Piezoelectric wafer active sensors (PWAS) used in structural health monitoring (SHM) applications are able to detect structural damage using Lamb waves. PWAS are small, lightweight, unobtrusive and inexpensive. They achieve direct transduction between electric and elastic wave energies. PWAS are charge mode sensors and can be used as both transmitters and receivers. The focus of this paper is to find a suitable in situ piezoelectric active sensor for sending and receiving Lamb waves to be used in the SHM of structures with a curved surface. Current SHM technology uses brittle piezoceramic (PZT) wafer active sensors. Since piezoceramics are brittle, this approach could only be used on flat surfaces. The motivation of our research was to explore the use of flexible piezoelectric materials, e.g. piezoelastic polymers such as PVDF. However, PVDF stiffness is orders of magnitude lower than the PZT stiffness, and hence PVDF Lamb wave transmitters are much weaker than PZT transmitters. Thus, our research proceeded in two main directions: (a) to model and understand how piezoelectric material properties affect the behaviour of piezoelectric wafer active sensors; and (b) to perform experiments to test the capabilities of the flexible PVDF PWAS in comparison with those of stiffer but brittle PZT PWAS. We have shown that, with appropriate signal amplification, PVDF PWAS can perform the same Lamb wave transmission and reception functions currently performed by PZT PWAS. The experimental results of PZT-PWAS and PVDF-PWAS have been compared with a conventional strain gauge. The theoretical and experimental results in this study gave a basic demonstration of the piezoelectricity of PZT-PWAS and PVDF-PWAS.

Structural identification and damage diagnosis using self-sensing piezo-impedance transducers

Abstract: The use of smart materials, such as lead zirconate titanate (PZT), has accelerated developments in the fields of structural identification and automated structural health monitoring (SHM). One such technique that has made much progress is the electro-mechanical impedance (EMI) technique, which employs self-sensing piezo-impedance transducers. In this technique, a PZT patch is surface bonded to the structure to be monitored and its corresponding electro-mechanical admittance signature is used for damage detection. This paper introduces a new method for identifying structures from the measured admittance signatures in terms of equivalent structural parameters, whereby the identified parameters are used for damage characterization. The new method has been applied to a truss, a beam and a concrete cube, and found to be able to successfully perform structural identification and damage diagnosis. In addition, several advantages have been ascertained in comparison with the conventional, non-parametric statistical methods.

Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural Networks

Abstract: A pattern recognition approach for structural health monitoring (SHM) is presented that uses damage-induced changes in Ritz vectors as the features to characterize the damage patterns defined by the corresponding locations and severity of damage. Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology.

Structural health monitoring

Abstract: Foreword Chapter 1 Introduction to SHM (Daniel L Balageas) Chapter 2 Vibration-based techniques for SHM (Claus-Peter Fritzen) Chapter 3 Fiber-optics sensors (Alfredo Guemes and Jose M Menendez) Chapter 4 SHM with piezoelectric sensors (Philippe Guy and Thomas Monnier) Chapter 5 SHM using electrical resistance (Michelle Salvia and Jean-Christophe Abry) Chapter 6 Low frequency electromagnetic techniques (Michel B Lemistre) Chapter 7 Capacitive methods for SHM in civil engineering (Xavier Derobert and Jean Iaquinta) Short Bibliographies of the Contributors Index

Distributed structural health monitoring system based on smart wireless sensor and multi-agent technology

Abstract: This paper presents a new parallel distributed structural health monitoring technology based on the wireless sensor network and multi-agent system for large scale engineering structures. The basic idea of this new technology is that of adopting the smart wireless sensor with on-board microprocessor to form the monitoring sensor network and the multi-agent technology to manage the whole health monitoring system. Using this technology, the health monitoring system becomes a distributing parallel system instead of a serial system with all processing work done by the central computer. The functions, the reliability, the flexibility and the speed of the whole system will be greatly improved. In addition, with wireless communication links instead of wires, the system weight and complexity will be lowered. In this paper, the distributed smart wireless sensor network is designed first based on the Berkeley Mote Mica wireless sensor platform. Two kinds of sensor have been adopted: piezoelectric sensors and electric resistance wires. They are connected to a Mica MPR board though a designed charge amplifier circuit or bridge circuit and MTS101 board. Seven kinds of agents are defined for the structural health monitoring system. A distributed health monitoring architecture based on the defined agents is proposed. Finally, a composite structural health monitoring system based on a Mica wireless platform and multi-agent technology is developed to evaluate the efficacy of the new technology. The developed system can successfully monitor the concentrated load position or a loose bolt position.

Structural Health Monitoring System for the Shandong Binzhou Yellow River Highway Bridge

Abstract: The Binzhou Yellow River Highway Bridge is a cable-stayed bridge in China. A structural health monitoring system was implemented on this bridge during its construction for monitoring its structural health status and assessing its safety for long-term service. This paper describes the design, implementation and functions of this system, and presents the measured responses of the bridge subjected to moving vehicle loads. The system includes a sensor module, a data acquisition module, a wired and wireless data transmit module, a structural analysis module, a database module, and a warning module. It is integrated by using LabVIEW software and can be remotely operated via Internet. After two years of service, the system operates well and confirms the current reliability of the bridge.

Towards autonomous sensing

Abstract: For some time, the smart materials and structures community has focused on transducer effects, and the closest advance into actually having the "structure" show signs of intelligence is implementing adaptive control into a smart structure. Here we examine taking this a step further by attempting to combine embedded computing into a smart structure system. The system of focus is based on integrated structural health monitoring of a panel which consists of a completely wireless, active sensing systems with embedded electronics. We propose and discuss an integrated autonomous sensor "patch" that contains the following key elements: power harvesting from ambient vibration and temperature gradients, a battery charging circuit, local computing and memory, active sensors, and wireless transmission. These elements should be autonomous, self contained, and unobtrusive compared to the system being monitored. Each of these elements is discussed as a part of an integrated system to be used in structural health monitoring applications.

Distributed computing strategy for structural health monitoring

Abstract: Monitoring of complex structures to provide real-time safety and reliability information regarding the structure poses significant technical challenges To detect damage in large civil infrastructure systems, densely distributed sensors are expected to be required Use of traditional wired sensors is challenging for such applications because of the cost and difficulty in deploying and maintaining a large wiring plant Using wireless sensor network is also difficult because large amounts of measured data need to be transferred to a central station The bandwidth and power requirement to transfer these data may easily exceed the limit of the wireless sensor Recently rapid advances in smart sensor technologies have made damage detection using a dense array of sensors feasible The essential feature of a smart sensor is the on-board microprocessor, which allows smart sensors to make decisions, perform computation, save data locally, etc By conducting a portion of the computation at the sensor level, only limited information needs to be transferred back to a central station However, damage detection algorithms which can take advantage of the distributed computing environment offered by smart sensors are currently limited In this paper, a new distributed computing strategy for structural health monitoring is proposed that is suitable for implementation on a network of densely distributed smart sensors In this approach, a hierarchical strategy is proposed in which adjacent smart sensors are grouped together to form sensor communities A flexibility-based damage detection method is employed to evaluate the condition of the local elements within these communities by utilizing only locally measured information The damage detection results in these communities are then communicated with the surrounding communities and sent back to a central station Numerical simulation demonstrates that the proposed approach works well for both single and multiple damage scenarios Copyright © 2005 John Wiley & Sons, Ltd

Smart Material Systems and MEMS: Design and Development Methodologies

Abstract: Preface. About the Authors. PART 1: FUNDAMENTALS. 1. Introduction to Smart Systems. 1.1 Components of a smart system. 1.2 Evolution of smart materials and structures. 1.3 Application areas for smart systems. 1.4 Organization of the book. References. 2. Processing of Smart Materials. 2.1 Introduction. 2.2 Semiconductors and their processing. 2.3 Metals and metallization techniques. 2.4 Ceramics. 2.5 Silicon micromachining techniques. 2.6 Polymers and their synthesis. 2.7 UV radiation curing of polymers. 2.8 Deposition techniques for polymer thin films. 2.9 Properties and synthesis of carbon nanotubes. References. PART 2: DESIGN PRINCIPLES. 3. Sensors for Smart Systems. 3.1 Introduction. 3.2 Conductometric sensors. 3.3 Capacitive sensors. 3.4 Piezoelectric sensors. 3.5 Magnetostrictive sensors. 3.6 Piezoresistive sensors. 3.7 Optical sensors. 3.8 Resonant sensors. 3.9 Semiconductor-based sensors. 3.10 Acoustic sensors. 3.11 Polymeric sensors. 3.12 Carbon nanotube sensors. References. 4. Actuators for Smart Systems. 4.1 Introduction. 4.2 Electrostatic transducers. 4.3 Electromagnetic transducers. 4.4 Electrodynamic transducers. 4.5 Piezoelectric transducers. 4.6 Electrostrictive transducers. 4.7 Magnetostrictive transducers. 4.8 Electrothermal actuators. 4.9 Comparison of actuation schemes. References. 5. Design Examples for Sensors and Actuators. 5.1 Introduction. 5.2 Piezoelectric sensors. 5.3 MEMS IDT-based accelerometers. 5.4 Fiber-optic gyroscopes. 5.5 Piezoresistive pressure sensors. 5.6 SAW-based wireless strain sensors. 5.7 SAW-based chemical sensors. 5.8 Microfluidic systems. References. PART 3: MODELING TECHNIQUES. 6. Introductory Concepts in Modeling. 6.1 Introduction to the theory of elasticity. 6.2 Theory of laminated composites. 6.3 Introduction to wave propagation in structures. References. 7. Introduction to the Finite Element Method. 7.1 Introduction. 7.2 Variational principles. 7.3 Energy functionals and variational operator. 7.4 Weak form of the governing differential equation. 7.5 Some basic energy theorems. 7.6 Finite element method. 7.7 Computational aspects in the finite element method. 7.8 Superconvergent finite element formulation. 7.9 Spectral finite element formulation. References. 8. Modeling of Smart Sensors and Actuators. 8.1 Introduction. 8.2 Finite element modeling of a 3-D composite laminate with embedded piezoelectric sensors and actuators. 8.3 Superconvergent smart thin-walled box beam element. 8.4 Modeling of magnetostrictive sensors and actuators. 8.5 Modeling of micro electromechanical systems. 8.6 Modeling of carbon nanotubes (CNTs). References. 9. Active Control Techniques. 9.1 Introduction. 9.2 Mathematical models for control theory. 9.3 Stability of control system. 9.4 Design concepts and methodology. 9.5 Modal order reduction. 9.6 Active control of vibration and waves due to broadband excitation. References. PART 4: FABRICATION METHODS AND APPLICATIONS. 10. Silicon Fabrication Techniques for MEMS. 10.1 Introduction. 10.2 Fabrication processes for silicon MEMS. 10.3 Deposition techniques for thin films in MEMS. 10.4 Bulk micromachining for silicon-based MEMS. 10.5 Silicon surface micromachining. 10.6 Processing by both bulk and surface micromachining. 10.7 LIGA process. References. 11. Polymeric MEMS Fabrication Techniques. 11.1 Introduction. 11.2 Microstereolithography. 11.3 Micromolding of polymeric 3-D structures. 11.4 Incorporation of metals and ceramics by polymeric processes. 11.5 Combined silicon and polymer structures. References. 12. Integration and Packaging of Smart Microsystems. 12.1 Integration of MEMS and microelectronics. 12.2 MEMS packaging. 12.3 Packaging techniques. 12.4 Reliability and key failure mechanisms. 12.5 Issues in packaging of microsystems. References. 13. Fabrication Examples of Smart Microsystems. 13.1 Introduction. 13.2 PVDF transducers. 13.3 SAW accelerometer. 13.4 Chemical and biosensors. 13.5 Polymeric fabrication of a microfluidic system. References. 14. Structural Health Monitoring Applications. 14.1 Introduction. 14.2 Structural health monitoring of composite wing-type structures using magnetostrictive sensors/actuators. 14.3 Assesment of damage severity and health monitoring using PZT sensors/actuators. 14.4 Actuation of DCB specimen under Mode-II dynamic loading. 14.5 Wireless MEMS-IDT microsensors for health monitoring of structures and systems. References. 15. Vibration and Noise-Control Applications. 15.1 Introduction. 15.2 Active vibration control in a thin-walled box beam. 15.3 Active noise control of structure-borne vibration and noise in a helicopter cabin. References. Index.

Experimental modal analysis of an aircraft model wing by embedded fiber Bragg grating sensors

Abstract: A critical issue in practical structural health monitoring is related to the capability of proper sensing systems integrated within the host structures to detect, identify, and localize damage generation. To this aim, many techniques have been proposed involving dynamic measurements such as modal analysis, acoustic emission, and ultrasonics. This paper relies on the use of embedded fiber Bragg grating sensors for performing an experimental modal analysis on a wing of an aircraft model. Time domain response of the embedded fiber-optic sensors induced by hammer impacts were acquired and transformed into the frequency domain. Using a classical technique based on the frequency transfer function, the first displacement and strain mode shapes of the wing have been retrieved in terms of natural frequencies and amplitudes. Experimental results confirm the excellent performances of this class of sensing devices to determine the modal behavior within complex structures compared with conventional accelerometer-based detection systems.