Structural Health Monitoring

Structural Health Monitoring (SHM) of aerospace structures has received significant interest as a core component of Condition Based Maintenance (CBM). This paper examines the possibility of using an ultrasonic guided wave system for damage detection in thin metallic plates. Acousto-Ultrasonic methods based on high frequency structural vibrations using piezoelectric transducers (PZT) have been developed to perform damage detection on thin metallic structures.The fundamentalLamb wave modes (AO&SO) travel into the structure and are reflected by the structural boundaries, discontinuities, and damage.Thus the presence of defect in the structure can be determined by studying their propagation and reflection. Laboratory level experiments have been carried out on thin Aluminum plates with angular, horizontal and vertical defect. This study provides significant insight into the problem of identifying localized damages in the structure using PZT and dispersion of signal after they interact wi...

Many European infrastructures dating back to '50 and '60 of the last century like bridges and viaducts are approaching the end of their design lifetime. In most European countries costs related to maintenance of infrastructures reach a quite high percentage of the construction budget and additional costs in terms of traffic delay are due to downtime related to the inspection and maintenance interventions. In the last 30 years, the rate of deterioration of these infrastructures has increased due to increased traffic loads, climate change related events and man-made hazards. A sustainable approach to infrastructures management over their lifecycle plays a key role in reducing the impact of mobility on safety (over 50 000 fatalities in EU per year) and the impact of greenhouse gases emission related to fossil fuels. The events related to the recent collapse of the Morandi bridge in Italy tragically highlighted the sheer need to improve resilience of aging transport infrastructures, in order to increase the safety for people and goods and to reduce losses of functionality and the related consequences. In this focus Structural Health Monitoring (SHM) is one of the key strategies with a great potential to provide a new approach to performance assessment and maintenance over the life cycle for an efficient, safe, resilient and sustainable management of the infrastructures. In this paper research efforts, needs and challenges in terms of performance monitoring, assessment and standardization are described and discussed.

Distributed Optical Fiber Sensors (DOFS) are strain measuring tools whose potential related to the civil engineering field has been discovered in the latest years only. A unique utility lies in the possibility of bonding these extremely thin sensors to steel reinforcement bars which will later be embedded in concrete elements (RC) in order to monitor its mechanical strains. The present paper presents the results of an experimental campaign that saw the tensing of two RC tensile members (ties) with DOFS-instrumented steel rebars providing strain readings every 7.5mm. Their cracking behavior allows to gain insight on the steel rebar's strain profile when surrounded by cracking concrete. The described results are novel as measurements that are accurate, completely distributed, experimentally extracted, both before and after concrete cracking, have been impossible up until now. The internal strain measurements are additionally integrated with an external Digital Image Correlation (DIC) monitoring which provides data on the displacements and strains of the members' surfaces. The present research represents the beginning of an in-depth experimental campaign aimed at providing reinforcement strain data for multiple loaded RC ties encompassing different geometrical features (concrete cover, reinforcement ratio, bar diameter).

Railway infrastructure maintenance is essential in implementing the transportation system. Most of these railway bridges have suffered gradual deterioration over time. Predictive structural health monitoring (SHM) is required by installing instrumentation sensors on railway bridges to determine the condition of the railway bridge infrastructure at the site. This research aims to analyze and assess the existing condition of steel railway bridges to understand the load-deformation characteristics, bearing capacity, and dynamic response of the structure. This paper describes a valuable method for assessing the condition of steel railway bridges during operation. This paper presents a direct analysis of the steel railway bridge structure, with a span of 40.00 meters, a width of 4.40 meters, and a height of 6.60 meters. The steel structure railway bridge is modeled in 3D in detail, and numerical analysis is carried out using finite element analysis based on input parameters obtained from manual field measurements and instrumentation sensors. The expected result of the development of this SHM System is to know the performance of the steel railway bridge structure in real-time via the dashboard display. The results showed that the carrying capacity of the railway bridge was in a relatively safe condition. This case study may help practice engineers and researchers in future research. It can be a valuable reference for future research in developing and applying such a system to a typical case.

In the current environment, structural health monitoring (SHM), has become increasingly important. The cost of sensors and connectivity has significantly decreased, allowing for remote data gathering for critical analysis and structure monitoring. This allows for the assessment and improvement of the structures' residual lifespan. The internet of things (IoT) is a network of intelligent sensors that combines the identification and detection followed by sending the different structural responses to remote computers for further analysis i.e., processing and monitoring. In this work, an integrated IoT platform for damage detection is proposed which includes an Arduino, Wi-Fi module, and sensors. The sensors gather responses from the host structure which follows a precise mathematical model is introduced to determine and measure the structural damage in comparison to the reactions of the structural member that is in good health. To determine the degree of damage, the responses recorded from the damaged and healthy beams are analyzed using the cross-correlation (CC) damage index. Moreover, the analysis carried out reveals the CC values are uploaded to the cloud, where, if the CC value is over the threshold limit, a mobile warning message is delivered.

tructural damages can change the structural properties, and consequently, changes of dynamic characteristics of structures, which can lead to outof-service conditions . Therefore, the unpredicted structural failure in civil structures can produce catastrophic collapse, economic costs, human injuries, and death . Thus, the focus on structural damage identification systems has been on the rise for civil engineering applications in order to evaluate the structural integrity using structural health monitoring (SHM) methods during construction and while in service. Consequently, effective and reliable non-destructive damage detection systems are very significant to monitor the structure for the occurrence, location, and severity of any damage . The majority of the vibration-based damage identification methods can be considered as some kind of pattern recognition due to their ability to find the differences between two types of data (e.g. before and after damage) . For instance, modal analysis is a vibrationbased approach that is used to identify damage from any changes in structural vibration characteristics with prior knowledge of the undamaged state [5]. In the modal analysis, any change in the structural physical parameters such as material changes and geometric properties changes can cause detectable effects in modal parameters . Blockchain technology is a growing reality of our modern

Vision-based techniques are frequently used to compute the dynamic deflections of bridges but they are rather computationally complicated and require demanding instrumentation. In this article, we show that it is possible to reconstruct the 2-D kinematics of flexible bridges using a simplified algorithm to analyze common video imagery. The only requirements are that the movement of the control points is clearly visible on the images and that next to each control point, there exist vertical and horizontal bridge elements defining the image scale. We applied this technique during controlled, forced excitations of a timber bridge that was stiff in the vertical but very flexible in the lateral axis because of cumulated damage. We used videos from low-cost cameras, in which the changes of the pixel coordinates of several control points during excitation events and their attenuation were clear. These videos were obtained during two annual structural health monitoring surveys using numerou...

Wireless Sensor Network applications are pulling in more research, particularly in energy saving techniques that are the centre purpose of analysts here. One of the fascinating and helpful uses of Wireless Sensor Networks is the movable target tracking. Wireless Sensor Networks is utilized to track movable target in a monitored territory and to report its location to the base station. . Identification of exact position and path travelled by the movable target over an area is a noteworthy energy customer procedure inside of Wireless Sensor Networks. In this paper, we consider a best design requirements to outline energy efficient cluster based routing protocol for Target Tracking in Wireless Sensor Networks (WSNs), with the assistance of this convention expand network lifetime of each node and decrease communication cost between nodes. Specifically, we deliberately outline a energy efficient target tracking protocol with thought of couple of imperative parameters, such as, aggregate remaining energy. At last, we abridge and finish up the paper with some future headings.

Non-destructive testing methods based on ultrasonic waves are one of the most popular methods for damage detection in structures. Of these ultrasonic waves Lamb waves are of particular interest for the inspection of large structures for various reasons. Therefore scattering of Lamb waves from flaws has generated a considerable amount of research over last couple of decades. Most of the work has been done using computational tools like Finite Element Methods and experimental technique. In this paper an analytical approach is presented to develop a fundamental understanding of the scattering of Lamb waves from geometric discontinuities in 2 dimensions. We have considered simplest of all geometric discontinuity -a step, as this fundamental understanding can easily be extended to corrosion or crack.

The aim of the work presented in this paper is to provide guidelines for extending the modeling capacities and improve quality and reliability of 2-D guided wave propagation models using commercially available finite element method (FEM) packages. Predictive simulation of ultrasonic nondestructive evaluation (NDE) and structural health monitoring (SHM) in realistic structures is challenging. Analytical methods can perform efficiently modeling of wave propagation are limited to simple geometries. Realistic structures with complicated geometries are usually modeled with the finite element method (FEM). Commercial FEM codes offer convenient built-in resources for automated meshing, frequency analysis, as well as time integration of dynamic events. We propose to develop FEM guidelines for 2-D Lamb wave propagation with a high level of accuracy. The proposed 2-D guided wave problem will be the pitch-catch arrangement in a full 3-D geometry plate involving guided waves between a transmitter piezoelectric wafer active sensor (PWAS) and receiver PWAS. In addition, corrosion damage is added to this problem to simulate the detection of damage, and assess the detectability threshold. The general approach is to run a series of FEM models. These FEM models will be compared with the experimental data and with our 1-D analytical homemade software.

In recent decades, swarm optimization methods have been employed to address various optimization problems in structural health monitoring (SHM). One of the widely recognized swarm-based algorithms, particle swarm optimization (PSO), has gained significant popularity and found extensive applications across diverse fields. However, it presents some limitations, such as the low convergence rate in the iterative process. The butterfly optimization algorithm (BOA) is a recently developed algorithm that has demonstrated its performance in solving a variety of optimization problems. In this research, a novel hybrid swarm optimization algorithm is introduced, integrating PSO and BOA, with the aim of enhancing its effectiveness. To overcome the limitations of the traditional Artificial Neural Network (ANN) technique and enhance its training performance, this new hybrid algorithm is integrated with ANN. The study offers valuable insights into the creation of a predictive model, known as PSO-BOA-ANN, for detecting structural damage. Input parameters for the model include natural frequencies, while the output parameter is the severity of the damage. To test the efficiency of the proposed technique, data were collected from a finite element model using a simulation tool, and from frequency response function (FRF) after experimental modal analysis for single and double cracked aluminum beams considering different crack depths. A comparative analysis was conducted between the results obtained from PSO, BOA, GA, and their respective combinations with ANN. The findings indicate that the novel PSO-BOA-ANN approach outperforms the other approaches in terms of accuracy when it comes to damage prediction.

Remote detection of structural aging, degradation phenomena and damage due to hazardous events is critical to ensure safety and reliability of civil or industrial structures. This is the motivation of the rapid development and increasing application of fully automated structural health monitoring (SHM) systems in civil engineering. Modal-based damage detection currently represents a popular approach for SHM of civil structures. In fact, it is a global method for damage detection and, as such, the measurement locations are not required to be close to the damage. However, damage sensitive features defined in terms of modal parameter estimates are also influenced by environmental and operational factors. Thus, neglecting this influence might jeopardize the reliability of the technology. In this framework, the present paper investigates the potential of modalbased SHM to detect earthquake damage at anchors of an atmospheric liquid storage tank. To this aim, the influences of bolt loosening at supports and of liquid level on modal parameters are investigated. The ultimate goal of the present study is the definition of criteria for the effective design and application of modal-based SHM to liquid storage tanks.

Structural aging, degradation phenomena, and damage due to hazardous events are common causes of failure in civil structures and infrastructures. The increasing need of extending the structure lifespan for sustainability and economic reasons motivated the rapid development of remote, fully automated structural health monitoring systems. Different approaches have been developed for damage detection based on the incoming data. Modal-based damage detection is probably one of the most popular procedures for structural health monitoring of civil structures, also thanks to the development of robust automated operational modal analysis algorithms in the last decade. However, the sensitivity of modal parameter estimates and the associated damage features to environmental and operational factors represents a significant drawback to the extensive application of this technology. Thus, effective damage detection cannot skip the preliminary compensation of the effect of those variables on modal ...

Recent earthquakes occurred in Italy and throughout the world have once again emphasized the critical role of health facilities for post-earthquake emergency management. Due to earthquake, several hospitals (L'Aquila 2009, Amatrice 2016) have lost their functionality because of damage to structural as well as non-structural members, equipment and installations. The primary role of hospitals after hazardous events requires the development of specific analysis and monitoring strategies aimed at quickly assessing their health conditions. Turning a hospital into a "Smart Health Facility" (SHF) allows remote assessment of the structural health; moreover, it can effectively support the mitigation of administrative and organizational vulnerability by acting on preparedness of the medical staff and supporting management and maintenance of structural as well as non-structural subsystems over time. Continuous monitoring of health and performance of hospitals can support the formulation of disaster mitigation plans and the definition of investment priorities to ensure the overall safety. In the present paper, some results of an on-going monitoring project for the main hospital in Campobasso (Southern Italy) are presented, focusing the attention on the response of the structure in operational conditions and after seismic events. Moreover, the results of experimental as well as operational modal analysis tests on a drug dispenser are discussed in order to assess their applicative perspectives for qualification of medical equipment in view of their seismic vulnerability assessment as non-structural elements.

Smart City is a cutting edge technology driven city aimed at making our everyday lives safer and more convenient. Like every city it is comprised of structures. To uphold their safety these structures have to undergo periodical maintenance checks. However, checks are done manually, thus consume a significant amount of time and financial resources. Remote Structural Health Monitoring (RSHM) is a perfect solution for a Smart City, however limited research is done in applying Internet of Things (IoT) sensor nodes for permanent RSHM in applications that use bolted joints as a main fastening method. Most of the proposed solutions are not suitable, since either additional structural modifications are required, they violate standards, provide poor wireless communication range or still require human intervention. Therefore, this paper proposes an IoT sensor node named TenSense M30, which leverages Long Range (LoRa) communication technology to enable wide communication coverage and long life for monitoring the health of bolted joints in a Smart City environment. TenSense M30 has a miniature footprint and can be used as an add-on to current structures. Mechanical, hardware and embedded designs are presented and evaluated by means of simulation and practical tests. Results show that TenSense M30 is capable of precisely monitoring the pre-tension load of bolted joints, has a secured non-line of sight communication channel with a distance of 800 meters, has an expected lifetime of greater than 5 years powered by a number of coin cell batteries, and is safe under the pre-tension load.

ensuring the safety, durability, and optimal performance of infrastructure. With the integration of Machine Learning (ML) and the Internet of Things (IoT), SHM has advanced significantly, enabling real-time monitoring, predictive maintenance, and data-driven decision-making. This book, "Machine Learning and IoT in Condition-Based Structural Health Monitoring," provides a comprehensive exploration of how these technologies are transforming SHM practices, enhancing efficiency, and minimizing structural failures. The book begins with Chapter 1, which provides an overview of Structural Health Monitoring, introducing fundamental concepts, monitoring techniques, and the importance of condition-based maintenance. It discusses traditional SHM methods and the need for advanced, technology-driven solutions. Chapter 2 delves into the Fundamentals of Machine Learning in SHM, explaining key ML algorithms, supervised and unsupervised learning models, and their applications in identifying structural anomalies and predicting failures. Chapter 3 explores the role of IoT in Real-Time Structural Health Monitoring, highlighting sensor networks, wireless data transmission, and cloud-based analytics that enable continuous structural assessment. It discusses various IoT-enabled devices and their role in SHM for real-time data collection and processing. Chapter 4 focuses on Data Processing and Implementation Strategies for condition-based SHM, covering data acquisition, feature extraction, signal processing techniques, and strategies to improve accuracy and efficiency in SHM applications. Advancing further, Chapter 5 examines the use of Deep Learning for Structural Health Diagnosis, detailing how artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) can enhance fault detection, classification, and predictive maintenance of structures. Chapter 6 shifts the focus to Structural Health Monitoring in the Digital Age, discussing IoT and Edge Computing Solutions, which enable decentralized and low-latency data processing, improving response times in SHM systems. Finally, Chapter 7 addresses Challenges, Future Trends, and Case Studies, highlighting practical implementation barriers, emerging innovations, and real-world applications of ML and IoT in SHM. Case studies demonstrate how these technologies are being successfully applied in diverse infrastructure sectors, including bridges, buildings, and transportation networks. This book serves as an essential resource for researchers, engineers, and professionals in structural health monitoring, providing a detailed understanding of how Machine Learning and IoT are revolutionizing the field. By integrating these cutting-edge technologies, the future of SHM is set to become more intelligent, predictive, and adaptive, ensuring the longevity and safety of critical structures worldwide.

Aircraft structures, whether metallic or composite, are subject to service damage which requires their periodic inspection and maintenance. While taking the aircraft out of service is quite costly, the assurance of structural integrity requires such inspection – and possible repair work based on the inspection results obtained. If damage is detected from an inspection, the decision whether to repair as well as the method of repair must be made on the basis of data relevant to the specific inspection method being used, and with an uncertainty that has been characterized as accurately as possible. This is an example of an application that is amenable to a Dynamic Data Driven Application System (DDDAS) solution. That is, data can be acquired dynamically, and compared to a model of the structure such that damage can be located and a determination made as to whether it requires further inspection and possible repair. Moreover, Bayesian methods allow the characterization of uncertainty, a...

The integration of wearable technology has transformed the healthcare industry, offering real-time monitoring of.. This research article provides a data-driven study of wearables, including fitness trackers, smartwatches and medical wearables with validated case studies and clinical data. The results show they were notable increased in physical activity, cardiovascular health and diabetes management. Disruptive frameworks such as AI-driven predictive health intervention and blockchain-based data security are also suggested through our proposals thus offering novel solutions to data privacy, device accuracy and sustainable long-term user engagement challenges reported in previous literature. In this paper, we used a scoping review to present the current state of the art on wearables in health care and discuss future directions.

The integration of wearable technology has transformed the healthcare industry, offering real-time monitoring of.. This research article provides a data-driven study of wearables, including fitness trackers, smartwatches and medical wearables with validated case studies and clinical data. The results show they were notable increased in physical activity, cardiovascular health and diabetes management. Disruptive frameworks such as AI-driven predictive health intervention and blockchain-based data security are also suggested through our proposals thus offering novel solutions to data privacy, device accuracy and sustainable long-term user engagement challenges reported in previous literature. In this paper, we used a scoping review to present the current state of the art on wearables in health care and discuss future directions.

In gearboxes, vibration stemming from load fluctuations and gear defects poses significant challenges. However, accessing and mounting vibration transducers in gearboxes can often be difficult. To address this, an experimental approach utilizing FFT analysis is employed to detect various types of gear tooth faults. By analysing vibration patterns, fluctuations in gear load gear faults can be identified effectively. This involves comparing signals from healthy and defective conditions using FFT analysis to trace sidebands of high-frequency vibrations. Validation is achieved by inputting data from an Accelerometer into LabVIEW, This comprehensive approach serves as a valuable tool for monitoring gear health under various operating conditions.

Carbon Nanotubes have been incorporated in a resin-based on the bi-functional epoxy precursor diglycidyl ether of bisphenol A (DGEBA). Electrical measurements allowed evaluating the electric percolation curve with a value of electric percolation threshold detected around 0.015% by weight of multi-wall carbon nanotubes (MWCNTs). It has been found, by electro-mechanical tests, that the Gauge Factor (G.F.) decreases with increasing the carbon nanotubes percentage. Furthermore, the presence of a residual resistivity allows diagnosing permanent damage in the epoxy matrix. High piezoresistive performance (G.F. = 4.7) of the Carbon Fiber Reinforced Panels (CFRP) coated with the epoxy resin containing 0.1 wt % of MWCNT, has been obtained.

Structural Health Monitoring (SHM) of composite structures leads to greater safety during operation and reduces the cost of regular inspections. Impact damage detection is an important SHM task. Since impact damage can significantly reduce the lifetime of composite structures, sensors for impact damage are of great interest. Carbon Fiber Sensors (CFSs) can be used to detect composite damage. CFSs are lightweight and compact, and they can be integrated during the manufacturing process. In our study, CFSs were manufactured from three types of carbon fiber tows and were integrated into different layers of the lay-up in order to investigate the influence on impact damage detection. The effect of mechanical loading and temperature change on the measured electrical resistance was investigated during cyclic flexural tests. It was revealed that, it is possible to distinguish between changes in measured signals due to impact and due mechanical loading. The change in the measured electrical s...

Passive safety of vehicle is one of the basic parameter defining the overall safety of the vehicle. It is determined primarily by the ability to protect the passengers in case of an unavoidable accident. It is given by ability of the structure (body) to absorb the kinetic energy of the impact by the conversion to potential energy in their deformation and/or damage. This ability is fixed at the present designs by the structure design (shape and choice of material). It is clear that such a construction meets safety requirements in different collision scenarios but in none optimally. It potentially means that cars are safe only in the cases of configured collisions that were considered during the design. There are unlimited amounts of real crash scenarios which show the need to adapt the design according to real crashes. Because of all these facts our intention is to develop composite beam that can adapt its force response as required by the actual situation. Fiber composite materials ...

This paper proposes a novel approach of utilizing Machine Learning (ML) models for embedded systems. This is demonstrated through the deployment of an ML model on a Gas Chromatography (GC) system, used for natural gas analysis. Unlike other schools of research that treat the analysis of chromatograms as image classification problems, this study treats chromatograms as time-series inputs. The trained model is ported to C programming language and deployed on an embedded system. Experimental results demonstrate the superior accuracy and robustness achieved by this ML based approach, when compared to the traditional analysis using heuristic algorithms. The execution time of the ML model on the embedded system is also found to be on par with analysis using heuristic algorithms.

Viral promotional advergames combine a promotional game with an advergame in a viral marketing campaign, aiming to encourage customer word of mouth, so that customers become willing promoters of the related brand. When designing a viral promotional advergame, marketers focus on game schemes and prizes to trigger players' intrinsic and extrinsic motivations and elicit certain behavioral responses, such as sharing personal data and forwarding the game. Therefore, the current research aimed to investigate how intrinsic playfulness and the extrinsic value of prizes affect people's behavioral responses. This research used data from pre-measure and post-measure assessments within a real viral marketing campaign. This field study examines the factors that lead to behavioral reactions in these types of games. In this connection, Poisson regression was used to investigate the effect of perceived playfulness and the perceived value of prizes on the variable of game forwarding, while logistic regression was used for the variable of personal data sharing.

Structural health monitoring (SHM) is crucial for monitoring structures performance, detecting the initiation of flaws and damages, and predicting structural life span. The dissertation emphasizes on developing analytical and numerical models for ultrasonics transduction between piezoelectric wafer active sensors (PWAS), and metallic and composite structures. The first objective of this research is studying the power and energy transduction between PWAS and structure for the aim of optimizing guided waves mode tuning and PWAS electromechanical (E/M) impedance for power-efficient SHM systems. Analytical models for power and energy were developed based on exact Lamb wave solution with application on multimodal Lamb wave situations that exist at high excitation frequencies and/or relatively thick structures. Experimental validation was conducted using Scanning Laser Doppler Vibrometer. The second objective of this work focuses on shear horizontal (SH) PWAS which are poled in the thickness-shear direction (d 35 mode). Analytical and finite element predictive models of the E/M impedance of the free and bonded SH-PWAS were developed. Next, the wave propagation method has been considered for isotropic materials. Finally, the power and energy of SH waves were analytically modeled and a MATLAB graphical user interface (GUI) was developed for determining the phase and group velocities, modeshapes, and the energy of SH waves. vi The third objective focuses on guided wave propagation in composites. The transfer matrix method (TMM) has been used to calculate dispersion curves of guided waves in composites. TMM suffers numerical instability at high frequency-thickness values, especially in multilayered composites. A method of using stiffness matrix method was investigated to overcome instability. A procedure of using combined stiffness transfer matrix method (STMM) was presented and coded in MATLAB. This was followed by a comparative study between commonly used methods for the calculation of ultrasonic guided waves in composites, e.g. global matrix method (GMM), semianalytical finite element (SAFE).

In recent years, damage detection has been an important issue in the condition assessment of structures. This research presents a new method for the detection of damaged members in truss bridges under moving load using the time history response and influence line curves of the members. For this reason, two different Finite Element (FE) models of truss bridges under moving load with different damage scenarios have been investigated. The damaged members are detected by adapting the difference curve shape of displacement responses obtained from the intact and damaged models to the axial force influence line curve shape of these members. The results demonstrate that when a member of a truss bridge is damaged, the difference curve of displacement responses is similar in shape to the influence line curve of the damaged member. It should be noted that the proposed method can accurately diagnose the damaged members with the displacement response of only one desired point of the truss bridge.

In our experiments with inspection of copper specimens it appeared that when transmitting ultrasound pulses in one frequency band in the immersed solids the received echoes had a broad frequency spectrum containing both the fundamental and higher harmonic frequency bands. The setup of the experiments consisted of two ultrasonic transducers with different center frequencies and bandwidths. A narrowband transmitting transducer had a lower center frequency and was excited with a pulse, while a broad band transducer with higher center frequency was used as a receiver. The higher resonances of the transmitter excited by the broadband pulse resulted in the higher harmonics. It is known from medical ultrasonic imaging that the high harmonics due to the nonlinearitiy of wave propagation can be combined to enhance the signal to noise ratio and the resolution in B-scan images. Using the similar idea the paper presents an attempt to apply such technique to the NDE for improving the quality of ultrasound B-scans acquired in immersion inspection of copper. To enhance the signal to noise ratio, a novel method for processing signals is proposed in the paper. The method implements 2-D matched filters for filtering B-scans. The algorithm uses a prototype signal, a B-scan region selected from the acquired ultrasonic data in a selected frequency band as the prototype signal for designing a matched filter. The resulting filter was used to process individual B-scans and then a C-scan was extracted from the processed B-scans. The ultrasonic data used were acquired in the inspection of electron beam welds in copper canisters for spent nuclear fuel, and processed by the proposed algorithm with the aim of enhancing the echoes from voids and porosity masked by backscattering from the weld structure.

Synthetic aperture focusing technique (SAFT) has been known since late 70s when it was introduced and evaluated in the application to nuclear power plants by Southwest Research Institute. In theory, SAFT is capable of realizing the theoretical resolution potential of ultrasonic waves and considerably improving the signal-to-noise-ratio of the ultrasonic image of coarse-grained materials. In its most often implemented version based on delay-and-sum operations in time, SAFT mimics an acoustic lens used for focusing ultrasonic beams at a desired point in the region of interest. However, the frequency-domain implementations of the synthetic aperture technique, developed in radar and seismology, offer substantial advantages in terms of performance and computational effort. This paper presents results obtained using a frequency-domain implementation of SAFT inspired by the migration concepts used in seismology. The general structure of the algorithm based on the phase-shift operation in the frequency-wavenumber domain is outlined first. The proposed algorithm can be applied for layered media, which means that it is suitable both for contact and immersion ultrasonic inspection. A brief theoretical introduction is followed by the presentation of evaluation results. The algorithm was applied for post-processing of ultrasonic data acquired in inspection of coarse grained metals (copper). The evaluation is made using the ultrasonic data obtained from the inspection of test specimens with artificial defects (side drilled holes and flat bottom holes). The performance of SAFT expressed in terms of its spatial resolution and grain noise suppression is studied.

In this paper we present results of the research aimed at designing and manufacturing a prototype of a SHM system that employs Phased Arrays (PA) for generating and receiving Lamb Waves (LW). A linear array was considered first but it appeared that an unequivocal localization of damage on a plane panel requires a 2D array's topology. In this paper we present a new method for theoretical, numerical and experimental investigations of various 2D arrays' topologies for SHM of planar structures. The theoretical evaluation is performed using the frequency-dependent transfer function that affects propagation of Lamb waves (LWs) through the dispersive medium and enables investigation of the arrays' performance for a defined excitation signal. The numerical simulations are conducted using local interaction simulation approach (LISA) implemented on the NVIDIA® CUDA® graphical processing unit (GPU), which considerably accelerates 3D simulations of LWs propagation in a short time period. Finally, scanning laser vibrometer is used to sense the LWs excited by PZT transducers, in multiple points corresponding to the locations of the 2D array elements. In this way performance of various array topologies can be evaluated experimentally in the reception mode without the need of physical prototype -a change of topology requires only straightforward modification of the measurement points' distribution at the tested plate.

In our experiments with inspection of copper specimens it appeared that when transmitting ultrasound pulses in one frequency band in the immersed solids the received echoes had a broad frequency spectrum containing both the fundamental and higher harmonic frequency bands. The setup of the experiments consisted of two ultrasonic transducers with different center frequencies and bandwidths. A narrowband transmitting transducer had a lower center frequency and was excited with a pulse, while a broad band transducer with higher center frequency was used as a receiver. The higher resonances of the transmitter excited by the broadband pulse resulted in the higher harmonics. It is known from medical ultrasonic imaging that the high harmonics due to the nonlinearitiy of wave propagation can be combined to enhance the signal to noise ratio and the resolution in B-scan images. Using the similar idea the paper presents an attempt to apply such technique to the NDE for improving the quality of ultrasound B-scans acquired in immersion inspection of copper. To enhance the signal to noise ratio, a novel method for processing signals is proposed in the paper. The method implements 2-D matched filters for filtering B-scans. The algorithm uses a prototype signal, a B-scan region selected from the acquired ultrasonic data in a selected frequency band as the prototype signal for designing a matched filter. The resulting filter was used to process individual B-scans and then a C-scan was extracted from the processed B-scans. The ultrasonic data used were acquired in the inspection of electron beam welds in copper canisters for spent nuclear fuel, and processed by the proposed algorithm with the aim of enhancing the echoes from voids and porosity masked by backscattering from the weld structure.

Phased arrays are relatively new tools that have been introduced to NDE in the recent decennium. Phased arrays have unquestionable advantages and open new possibilities of realtime ultrasonic imaging in NDE. However, the common way of designing focusing laws is based on the point source assumption, that is, it is assumed that the element size can be neglected. Since such assumption is valid in far field only, it may in many cases lead to degraded lateral resolution. Extended synthetic aperture focusing technique (ESAFT) that was proposed recently [1] compensates for the diffraction effects of a finite sized transducer both in transmission and reception. It has been shown that the lateral resolution offered by ESAFT is superior to the classical SAFT. Here, we propose a generalization of the ESAFT technique for phased arrays resulting in the Optimal Linear Receive Beamformer (OLIRB). The deconvolution concept used in ESAFT has been modified in OLIRB to suit phased arrays. A multidimensional linear filter optimized using the mean square error criterion is applied for compensating diffraction effects and delays introduced by the apertures used in transmission and reception. The filter replaces conventional delay and sum operation in ultrasonic system and, due to its parallel structure it can be used for processing ultrasonic signals acquired from array elements in real time. Performance of the OLIRB algorithm is demonstrated on simulated data and verified on data acquired from a real array system.

The guided waves -based damage detection and localization methods are increasingly used as tools for structural health monitoring and non-destructive damage detection. Propagating waves are very sensitive to both structure damages and boundaries, however Lamb waves propagation is a rater complex phenomenon and many factors like sensors/actuators location, electronic system design, sensor shape, frequency range of excitation and more, should be taken into account. In this paper sensor study on the transducer used for Lamb waves generation and acquisition has been presented. A measured signal includes not only a structure response, but also it depends on a transducer characteristics, coupling agent and more. Actuator/sensor selection based only on specification sheet can be insufficient, so time and frequency analysis have been applied to signals acquired when an examined transducer was attached to a plate. Ceramic multilayer rings and plates have been tested to be used in

Recently, an intensive research activity has been observed concerning the application of guided waves to long range ultrasound (LRUT) and structural health monitoring. Guided waves propagating in plates, known as Lamb waves, have appeared to be suitable for monitoring and condition evaluation of planar structures. Lamb waves can be generated using different types of actuators including piezoelectric or electromagnetic acoustic transducers. In this paper a novel type of piezoelectric transducers is presented based on macro-fiber piezocomposite (MFC). Contrary to the previously presented MFC transducers the transducers presented here are provided with interdigital electrodes matched to certain wavelength. Two different designs of interdigital transducers (IDT) made of MFC substrate are presented in the paper together with the results of numerical simulations and experimental tests.

Lamb waves have shown a great potential in structural health monitoring (SHM) of thin plates that are frequently used in engineering structures. Dense transducer networks or active ultrasonic arrays can be employed to generate and receive Lamb waves. Active arrays, however, enable beam steering and can work as spatial filters enabling assessment of large plate areas from a single fixed position. In this paper a novel method for selective focusing of Lamb waves is presented. The algorithm is an extension of the DORT method (French acronym for decomposition of time-reversal operator) where the continuous wavelet transform (CWT) is used for the time-frequency representation (TFR) of nonstationary signals instead of the discrete Fourier transform. An application of the proposed method to self focusing of Lamb waves in an aluminum plate is demonstrated both for a linear and a 2D star-shaped array. The 2D star-like array was designed to eliminate the effect of ambiguous mirrored images encountered for linear arrays. The results obtained with both arrays are presented and compared in the paper. It is shown that the decomposition of the time reversal operator obtained with the proposed method enabled separating point like scatters in the aluminum plate and allowed to focus Lamb waves at a desired point.

Uniform circular arrays (UCAs) with 360° azimuthal coverage are very useful for structural health monitoring (SHM) of large planar structures. Difficulties encountered when working with Lamb waves include their dispersive and multi-modal nature. In this article three different methods for estimating the direction-of-arrival (DOA) of incoming Lamb waves are compared and verified using simulated and experimental data. The previously proposed phase-mode excitation-based beamformer is compared with two high-resolution spectral estimation techniques, the Capon method and the multiple signal classification method. Design consideration and guidelines for UCAs are presented. To experimentally evaluate the performance of the considered DOA methods a prototype array consisting of 16 separate pinducers arranged in a ring was constructed. The array was provided with an analog multiplexer that enabled recording signals received by the individual elements using a digital oscilloscope. The array w...