Search

Real-time monitoring of structural health status is very important for supersonic aircraft, since the long-term operation in harsh service environments, such as high temperatures, can cause potential damage. Structural health monitoring (SHM) based on lamb wave has been approved to be a critical technique for the reliable operation of supersonic aircraft, but is limited by the operating temperature of conventional piezoelectric sensors, which makes it difficult to meet the high-temperature monitoring requirements. The BiFeO 3 –BaTiO 3 ceramics exhibit excellent piezoelectric properties and high Curie temperature (T c), making them promising for high-temperature SHM of supersonic aircraft. Here, we fabricated a piezoelectric sensor using 0.67BiFeO 3 -0.33BaTiO 3 -0.25%molMnO 2 (BF33BTMN-025) ceramic, which exhibits a piezoelectric charge constant (d 33) of 135 pC/N at room temperature and a T c of 448 °C. The BF33BTMN-025 ceramic sensor operates effectively at temperatures up to 200 °C and maintains stable damage monitoring sensitivity as well. Compared with Pb(Zr, Ti)O 3 (PZT), BaTiO 3 (BTO) and other piezoelectric sensors commonly used in SHM field, the BF33BTMN-025 ceramic sensor has a higher operating temperature and a comparable sensitivity. Our work demonstrates that the BF33BTMN-025 ceramic sensor can be employed as piezoelectric sensor for high-temperature SHM, which has a wide application potential in the field of real-time monitoring of supersonic aircraft. [ABSTRACT FROM AUTHOR]

Carbon fiber-reinforced polymers (CFRPs) are widely used in the fabrication of solid rocket motor casings due to their exceptional performance. However, the bonding interface between CFRP and viscoelastic materials (rubber) is prone to debonding damage during service and storage under complex environmental conditions, which poses a significant threat to the structural integrity and reliability of the engine. Existing nondestructive testing (NDT) methods, such as X-ray imaging, infrared thermography, and ultrasonic testing, although somewhat effective, exhibit significant limitations in detecting interfacial defects in deep or multilayered composite materials, particularly under the challenging conditions of service and storage. This study proposes an innovative method based on active Lamb wave energy analysis and introduces the Damage Evolution Factor (DEF), specifically designed to detect and evaluate interfacial debonding defects in CFRP–rubber bonded structures within solid rocket motors during service and storage. Through numerical simulations and experimental validation, we selected the A0 mode Lamb wave, which is more sensitive to interfacial damage, as the incident wave and excited it on the surface of the structure. Displacement time-history response signals at observation points under different damage models were extracted and analyzed, and DEF values were calculated. The results show that DEF values increase with the size of the interfacial debonding damage. Similar trends were observed in experimental studies, further validating the effectiveness of this method and demonstrating that DEF can be used for the quantitative evaluation of interfacial debonding defects in CFRP–rubber bilayer bonded structures. [ABSTRACT FROM AUTHOR]

Guided waves propagating in mechanical structures have proved to be an essential technique for applications, such as structural health monitoring. However, it is a well-known problem that when using non-stationary guided wave signals, dispersion, and high-order vibrational modes are excited, it becomes cumbersome to detect and identify relevant information. A typical method for the characterization of these non-stationary signals is based on time–frequency (TF) mapping techniques. This method produces 2D images, allowing the study of specific vibration modes and their evolution over time. However, this approach has low resolution, increases the size of the data, and introduces redundant information, making it difficult to extract relevant features for their accurate identification and classification. This paper presents a method for identifying discontinuities by analyzing the data in the TF maps of Lamb wave signals. Singular Value Decomposition (SVD) for low-rank optimization and then perform foreground feature extraction on the maps were proposed. These foreground features are then analyzed using Principal Component Analysis (PCA). Unlike traditional PCA, which operates on vectorized images, our approach focuses on the correlation between coordinates within the maps. This modification enhances feature detection and enables the classification of discontinuities within the maps. To evaluate unsupervised clustering of the dimensionally reduced data obtained from PCA, we experimentally tested our method using broadband Lamb waves with various vibrational modes interacting with different types of discontinuity patterns in a thin aluminum plate. A Support Vector Machine (SVM) classifier was then implemented for classification. The results of the experimental data yielded good classification effectiveness within reasonably low computational time despite the large matrixes of the TF maps used. [ABSTRACT FROM AUTHOR]

The lowest-order shear horizontal (SH0) guided wave in a plate is crucial for nondestructive testing and structural health monitoring due to its nondispersive nature. However, mode conversion at the interface for the SH0 wave complicates the received information and increases the difficulty of signal analysis. In this work, an elastic metasurface is proposed to suppress mode conversion at the interface and to reflect the SH0 wave directionally. The metasurface comprises a series of rectangular elements with a specific phase gradient covering the 2 π phase range. The effectiveness of the proposed metasurface is confirmed through finite element simulations and experimental validation. The results indicate that the metasurface can significantly reduce the amplitude of the reflected lowest-order symmetric Lamb wave when compared to the reflection without the metasurface, while simultaneously allowing the reflected SH0 wave to propagate at the designed angle. Furthermore, the metasurface maintains its functionality for SH0 wave with incident angles varying from 0° to 25°. This study provides a viable approach for suppressing mode conversion and modulating the reflection behavior of SH0 wave, which is expected to find applications in nondestructive testing and structural health monitoring. [ABSTRACT FROM AUTHOR]

For practical engineering structures, fatigue is one of the main factors affecting their safety and durability. Under long-term service conditions, the minor damage will be affected by fatigue loading and expand to macroscopic cracks, affecting the structure's service performance. Based on the sensitivity of Lamb waves to minor and initial damage, a damage monitoring method for fatigue crack propagation is proposed. By carrying out fatigue crack propagation tests under constant amplitude loading, the Paris equation of 316L steel and damage signals at different crack growth stages were obtained. Combined with damage monitoring tests and finite element analysis, the relationship between the phase damage index (PDI), amplitude damage index (ADI), signal correlation coefficient, and fatigue crack propagation length was studied. Compared with PDI and ADI, the signal correlation coefficient is more sensitive to crack initiation, which can be selected as the damage monitoring index in the initial stage of crack growth. With the increase of fatigue crack propagation length, the peak time of the direct wave signal gradually moves backward, which shows an obvious phase change. In the whole fatigue crack growth stage, PDI and crack length show a monotonically changing trend. By using the stress intensity factor as the conversion parameter, a prediction model of the fatigue crack propagation rate based on PDI was established. Compared to the fatigue crack propagation rate measured by experiments, the relative error of the predicted results is 10%, which verifies the accuracy of the proposed damage monitoring method. [ABSTRACT FROM AUTHOR]

Ultrasonic guided waves, which are often generated and detected by piezoelectric transducers, are well established to monitor engineering structures. Wireless solutions are sought to eliminate cumbersome wire installation. This work proposes a method for remote ultrasonic-based structural health monitoring (SHM) using mechanoluminescence (ML). Propagating guided waves transmitted by a piezoelectric transducer attached to a structure induce elastic deformation that can be captured by elastico-ML. An ML coating composed of copper-doped zinc sulfide (ZnS:Cu) particles embedded in PVDF on a thin aluminium plate can be used to achieve the elastico-ML for the remote sensing of propagating guided waves. The simulation and experimental results indicated that a very high voltage would be required to reach the threshold pressure applied to the ML particles, which is about 1.5 MPa for ZnS particles. The high voltage was estimated to be 214 Vpp for surface waves and 750 Vpp for Lamb waves for the studied configuration. Several possible technical solutions are suggested for achieving ultrasonic-induced ML for future remote SHM systems. [ABSTRACT FROM AUTHOR]

Plate-like structures, including composite material plates, are extensively employed in major engineering structures such as aerospace, high-speed rail, and civil engineering. The growing utilization of curved panel structures and the increasing complexity of these structures have emphasized the need for flexible sensors capable of efficient signal front-end reception. Addressing the limitations of conventional sensors made of brittle piezoelectric materials in adhering to the surface of structures for health monitoring, a self-developed flexible dielectric elastomer-interdigital transducer (DE-IDT) array is employed to achieve damage imaging in isotropic aluminum curved panel structures and orthotropic composite material plates using linear frequency modulation (chirp) signal excitation. In addition, a proposed fusion method is utilized to obtain weighted imaging results, and evaluation and analysis are performed using relevant indicators. Experimental findings highlight the improved imaging accuracy achieved through phased array data fusion using the linear chirp signal, attributed to its wideband characteristics and ability to capture more damage signals. Moreover, the effectiveness of the circular DE-IDT array for damage detection and imaging in curved panel structures and composite material plates is successfully verified. [ABSTRACT FROM AUTHOR]

This paper presents a damage sparse imaging method using multipath-scattered Lamb waves. It leverages a large number of echoes and reverberations in the recorded signal that may be usually ignored in conventional methods. First, reflections of Lamb waves at free edges are viewed as waves transmitted from a virtual transducer which is located at the mirror point of the actual one. On this basis, an optimized transducers-layout strategy is proposed based on the multipath propagation model of the Lamb wave. Benefiting from that, the direct damage-scattered wave and several waves scattered by both the damage and edges could be separately identified in the time domain, and further, each wave could be matched with a sensing path (either actual or virtual) in the expanded sensor network. Subsequently, a dictionary is constructed from the Lamb wave propagation and scattering model. By solving the sparse reconstruction problem, the pixel value of each point in the region of interest is obtained, and the whole area can be finally visualized. The proposed method is validated using experiments conducted on an aluminum plate with simulated damages. Results show that the damages can be correctly detected and accurately localized with only a single transmitter–receiver pair. [ABSTRACT FROM AUTHOR]

Topological data analysis (TDA) is a powerful and promising tool for data analysis, but yet not exploited enough. It is a multidimensional method which can extract the topological features contained in a given dataset. An original TDA-based method allowing to monitor the health of structures when equipped with piezoelectric transducers (PZTs) is introduced here. Using a Lamb wave based Structural Health Monitoring (SHM) approach, it is shown that with specific pre-processing of the measured time-series data, the TDA (persistent homology) for damage detection and classification can be greatly improved. The TDA tool is first applied directly in a traditional manner in order to use homology classes to assess damage. After that, another method based on slicing the temporal data is developed to improve the persistence homology perception and to leverage topological descriptors to discriminate different damages. The dataset used to apply both methods comes from experimental campaigns performed on aeronautical composite plates with embedded PZTs where different damage types have been investigated such as delamination, impacts and stiffness reduction. The proposed approach enables to consider a priori physical information and provides a better way to classify damages than the traditional TDA approach. In summary, this article demonstrates that manipulating the topological the features of PZTs time-series signals using TDA provides an efficient mean to separate and classify the damage natures and opens the way for further developments on the use of TDA in SHM. [ABSTRACT FROM AUTHOR]

Composite plates are susceptible to various damages in complex conditions and working environments, which may reduce the reliability of the structure and threaten equipment and personal safety. Thus, the implementation of a robust online Structural health monitoring (SHM) system for these composite structures becomes imperative. To enhance reliability and safety, we introduce a robust online SHM system anchored by our newly developed damage detection Bayesian neural network (DD-BNN). The main contribution of this study lies in the DD-BNN to perform precise and reliable damage detection and localization in composite plates using only one actuator-receiver pair without any signal/feature pre-processing and human intervention. The proposed DD-BNN model innovatively combines probabilistic modeling with deep learning to address uncertainty in Lamb wave-based damage detection and model performance for composite plates, featuring a specialized probabilistic layer trained through Bayesian inference to efficiently encapsulate and manage uncertainty in model weights and activation. Notably, our method significantly simplifies the SHM system design and manual operation requirements. In addition, this approach not only reduces overfitting but also enhances robustness to noise, as confirmed by experiments on perturbation analysis of Gaussian and Poisson noise. [ABSTRACT FROM AUTHOR]

Delamination represents one of the most significant and dangerous damages in composite plates. Recently, many papers have presented the capability of structural health monitoring (SHM) techniques for the investigation of structural delamination with various shapes and thickness depths. However, few studies have been conducted regarding the utilization of convolutional neural network (CNN) methods for automating the non-destructive testing (NDT) techniques database to identify the delamination size and depth. In this paper, an automated system qualified for distinguishing between pristine and damaged structures and classifying three classes of delamination with various depths is presented. This system includes a proposed CNN model and the Lamb wave technique. In this work, a unidirectional composite plate with three samples of delamination inserted at different depths was prepared for numerical and experimental investigations. In the numerical part, the guided wave propagation and interaction with three samples of delamination were studied to observe how the delamination depth can affect the scattered and trapped waves over the delamination region. This numerical study was validated experimentally using an efficient ultrasonic guided waves technique. This technique involved piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV). Both numerical and experimental studies demonstrate that the delamination depth has a direct effect on the trapped waves' energy and distribution. Three different datasets were collected from the numerical and experimental studies, involving the numerical wavefield image dataset, experimental wavefield image dataset, and experimental wavenumber spectrum image dataset. These three datasets were used independently with the proposed CNN model to develop a system that can automatically classify four classes (pristine class and three different delamination classes). The results of all three datasets show the capability of the proposed CNN model for predicting the delamination depth with high accuracy. The proposed CNN model results of the three different datasets were validated using the GoogLeNet CNN. The results of both methods show an excellent agreement. The results proved the capability of the wavefield image and wavenumber spectrum datasets to be used as input data to the CNN for the detection of delamination depth. [ABSTRACT FROM AUTHOR]

The article outlines Insight's current awareness service, which covers a wide range of British and international publications, conference proceedings, and multimedia products related to non-destructive testing (NDT). It categorizes the literature search into 14 sections, providing resources for various NDT techniques, and invites comments and contributions for further enhancement, while also offering information on accessing papers.

Composite materials are widely used in most industries due to their high specific strength, specific stiffness, and their relatively lighter weight compared to other traditional materials. However, the presence of defects arising from manufacturing processes or during service loads can make these structures more susceptible to a diminished performance. Furthermore, the former defects are inevitable in composite structures, but they can be reduced. Each type of defect requires specific inspection techniques and configurations. In this work, a review of the different types of composites manufacturing processes and their corresponding resultant defects is presented with the various nondestructive evaluation techniques employed for these defects' characterization. The emphasis of this paper is on ultrasonic inspection and detection techniques for they present high sensitivity to surface/subsurface discontinuities, superior depth of ultrasonic penetration for flaw detection, feasibility on large scales, and instantaneous and detailed images production. Notably, noncontact ultrasonic testing techniques are also reviewed, air-coupled techniques in specific, and highlighted as a fine alternative to conventional contact inspection systems as they reduce the restrictions that coexist with the use of couplants. Moreover, these ultrasonic testing techniques are summarized to show the latest research progress achieved in the field of air-coupled ultrasonic inspection systems for manufacturing defects' monitoring in composite structures including delamination, porosity, dryness, waviness, and resin lack/excess. Finally, we highlight the type and central frequency of the transducers and experimental results present in literature and obtained in terms of both detection and size of the defects. [ABSTRACT FROM AUTHOR]

Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure's health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT). layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency. [ABSTRACT FROM AUTHOR]

Lamb wave-based damage quantification in large-scale composites has always been one of the concerning and intractable problems in aircraft structural health monitoring. In recent years, machine learning (ML) algorithms have been utilized to deeply explore the damage feature of Lamb wave signals, which aims to enhance the precision and accuracy of damage quantification. However, multi-damage quantification becomes one of the bottleneck problems because ML algorithms critically depend on the dataset. In this paper, a prioritizing selection and orderly permutation method is proposed to construct multi-damage dataset based on Born approximation principle, which shows the interaction between wave signals under multi- and single-damage conditions. Based on the multi-damage dataset, a multi-task deep learning algorithm is introduced to identify multiple damage, including the damage number, location, and size, in composite laminates. In the algorithm, a multi-branch 1D-convolution neural network framework, which includes a trunk network and branch networks is established to explore the damage features in Lamb wave scattering signals. Compared with single-task models, it has the ability to learn shared features for multiple tasks, effectively boosting the task results. The results show that the proposed multi-task learning (MTL) method saves 23.03% training time compared with the single-task learning method. In the task of quantifying multiple damage of composite laminate, the results of MTL are good for both the constructed test set and the measured test set, especially in the quantification of damage size, which shows the feasibility and reliability of this method. [ABSTRACT FROM AUTHOR]

This paper presents research on the application of ultrasonic-guided wave technology in corrosion defect identification, expounds the relevant ultrasonic-guided wave theories and the principle of ultrasonic-guided wave non-destructive testing of pipelines, and discusses the Lamb wave and shear horizontal wave mode selection that is commonly used in ultrasonic-guided wave corrosion detection. Furthermore, research progress in the field of ultrasonic-guided wave non-destructive testing (NDT) technology, i.e., regarding transducers, structural health monitoring, convolutional neural networks, machine learning, and other fields, is reviewed. Finally, the future prospects of ultrasonic-guided wave NDT technology are discussed. [ABSTRACT FROM AUTHOR]

This study conducted experimental and numerical investigations on piezoelectric wafer active sensors (PWASs) bonded to an aluminum plate to assess the impact of bonding degradation on Lamb wave generation. Three surface-bonded PWASs were examined, including one intentionally bonded with a reduced adhesive to create a defective bond. Thermal cyclic aging was applied, monitoring through laser Doppler vibrometry (LDV) and static capacitance measurements. The PWAS with the initially defective bond exhibited the poorest performance over aging cycles, emphasizing the significance of the initial bond condition. As debonding progressed, modifications in electromechanical behavior were observed, leading to a reduction in wave amplitude and distortion of the generated wave field, challenging the validity of existing analytical modeling of wave-tuning curves for perfectly bonded PWASs. Both numerical simulations and experimental observations substantiated this finding. In conclusion, this study highlights the imperative of a high-integrity bond for the proper functioning of a guided wave-based structural health monitoring (SHM) system, emphasizing ongoing challenges in assessing SHM performance. [ABSTRACT FROM AUTHOR]

Carbon fiber-reinforced polymers (CFRPs) are widely used in the fabrication of solid rocket motor casings due to their exceptional performance. However, the bonding interface between CFRP and viscoelastic materials (rubber) is prone to debonding damage during service and storage under complex environmental conditions, which poses a significant threat to the structural integrity and reliability of the engine. Existing nondestructive testing (NDT) methods, such as X-ray imaging, infrared thermography, and ultrasonic testing, although somewhat effective, exhibit significant limitations in detecting interfacial defects in deep or multilayered composite materials, particularly under the challenging conditions of service and storage. This study proposes an innovative method based on active Lamb wave energy analysis and introduces the Damage Evolution Factor (DEF), specifically designed to detect and evaluate interfacial debonding defects in CFRP–rubber bonded structures within solid rocket motors during service and storage. Through numerical simulations and experimental validation, we selected the A0 mode Lamb wave, which is more sensitive to interfacial damage, as the incident wave and excited it on the surface of the structure. Displacement time-history response signals at observation points under different damage models were extracted and analyzed, and DEF values were calculated. The results show that DEF values increase with the size of the interfacial debonding damage. Similar trends were observed in experimental studies, further validating the effectiveness of this method and demonstrating that DEF can be used for the quantitative evaluation of interfacial debonding defects in CFRP–rubber bilayer bonded structures.

To study the acoustic characteristics of sound scattered from laminated structures such as elastic plates and shells, it is usually required to solve the Lamb waves' dispersion equations. Many traditional root-finding methods such as bisection, the Newton–Raphson method, and the Muller method are not able to tackle the problem completely. A simple but powerful method named local peaks search (LPS) is proposed to overcome their drawbacks. Firstly, the non-zero part of the dispersion equation is defined as the dispersion function, and its reciprocal is used to transform the zeros (i.e., roots) into local peaks. Secondly, the chosen complex domain is discretized, and the coarse local domains where the local peaks exist are determined by the direct search method globally. Thirdly, the Muller method is applied to obtain the refined locations of local peaks. Lastly, in order to refine the results, a hierarchical scheme is designed and the iteration of the above procedures is implemented; the error is set to be 10−16 as the stop criteria. The accuracy of the LPS method is validated by comparing it with the bisection method for the problem of elastic plates in the vacuum. The acoustic echo structures are analyzed experimentally. By computation of Lamb waves' phase velocity, the critical angles are derived numerically and compared with the results acquired by an experiment using monostatic sound transducers. In this way, it is validated that the elastic scattered wave components are the highlights shown in the time-angle figure. Furthermore, the work can be applied for non-destructive testing, especially underwater structural health monitoring. [ABSTRACT FROM AUTHOR]

Although Lamb waves have found extensive use in structural damage detection, their practical applications remain limited. This limitation primarily arises from the intricate nature of Lamb wave propagation modes and the effect of temperature variations. Therefore, rather than directly inspecting and interpreting Lamb wave responses for insights into the structural health, this study proposes a novel approach, based on a two-step cointegration-based computation procedure, for structural damage evaluation using Lamb wave data represented as time series that exhibit some common trends. The first step involves the composition of Lamb wave series sharing a common upward (or downward) trend of temperature. In the second step, the cointegration analysis is applied for each group of Lamb wave series, which represents a certain condition of damage. So, a cointegration analysis model of Lamb wave series is created for each damage condition. The geometrical and statistical features of Lamb wave series and cointegration residual series are used for detecting and distinguishing damage conditions. These features include the shape, peak-to-peak amplitude, and variance of the series. The validity of this method is confirmed through its application to the Lamb wave data collected from both undamaged and damaged aluminium plates subjected to temperature fluctuations. The proposed approach can find its application not only in Lamb wave-based damage detection, but also in other structural health monitoring (SHM) systems where the data can be arranged in the form of sharing common environmental and/or operational trends. [ABSTRACT FROM AUTHOR]

Human health monitoring (HHM) is essential for continued daily task execution, as is structural health monitoring (SHM) for structures to ensure the continual performance of their designed tasks with optimal efficiency. The existence of damage in a structure affects its optimal use through stiffness deterioration. Damage of different forms could occur in a structure but have the singular objective of material degradation, leading to its underuse for a task. Guided wave ultrasonics has shown strength in detecting sundry damage in structures, but most of the damage monitored and detected is unfilled with substances. However, some damage could trap and accumulate substances that could hasten material degradation through corrosion activities under favorable conditions, especially in the oil and gas industry. This study used the ultrasonic-guided waves' pitch–catch inspection technique to identify damage filled with different materials. The assessment was based on the RMSD of the dominant Lamb wave mode's average maximum amplitude and the response signals' transmission coefficient (TC). A five-cycle tone burst of excitation signals of different frequencies was created to generate propagating Lamb waves in the structure. The fundamental antisymmetric mode was found to be more sensitive than the fundamental symmetric mode when detecting damage filled with various substances. At 80 kHz, the deviation of the current response signals from the baseline response signals due to different filled substances in the damage was distinct and decreased with increased fluid viscosity. Given that structures in the oil and gas sector are particularly susceptible to substance-induced damage, the outcomes of this study are paramount. [ABSTRACT FROM AUTHOR]

The multiple signal classification (MUSIC) algorithm is originally used for direction of arrival estimation and frequency identification. MUSIC-like algorithms have gained their popularity in structural health monitoring (SHM) owing to their super-resolution characteristic and the virtue of baseline independence. MUSIC essentially is a searching algorithm to find the best phase difference matching between the eigenvector from signal subspace and the presupposed steering vector. However, in the guided-wave-based SHM (in plate-like structure, the guided wave is Lamb wave), the dispersion effect will definitely lead to phase distortion. Therefore, Xu et al. presented a focusing MUSIC algorithm where the time reversal technique is adopted to eliminate the dispersion in the way of back propagation. Seeing that the phase difference information, which is necessary for MUSIC, disappears after back propagation, the damage scattered wave packet, obtained by window truncation, is forward propagated again at the constant group velocity to recover the time of flight information. Nevertheless, the forward propagation process is superfluous from the view of phase matching. In the presented forward-propagation-free focusing MUSIC (FPF-F MUSIC) algorithm (the simplified version of Xu's work), by revising the steering vector in the form of phase coincidence, the matching relation for MUSIC is modified from phase difference matching to phase alignment matching, so that the forward propagation and the steering vector traversal setting for every inspection point are all omitted. The damage localization performance of FPF-F MUSIC is verified on the aluminum plate with double through-hole and surface defects under uniform linear and circular arrays. [ABSTRACT FROM AUTHOR]

Ultrasonic attenuation measurements were conducted on cross-ply and quasi-isotropic lay-ups of eight types of carbon-fiber reinforced composites (CFRPs) using through-transmission methods with diffraction correction. Attenuation values were substantially higher than those of unidirectional composites and other structural materials. Wave modes, fiber distributions, matrix resins, and consolidation methods affected total attenuation. Transverse mode, quasi-isotropic lay-up, and polyimide and thermoplastic resins generally produced higher attenuation. No clear trends from the fiber distribution were revealed, indicating that it is not feasible presently to predict the attenuation of various lay-ups from the unidirectional values. That is, direct attenuation tests for different laminate lay-ups are needed. This work expanded the existing attenuation database by properly determining the attenuation coefficients of two additional layup types of CFRP laminates. Results showed the merit of ultrasonic attenuation measurements for quality control and structural health monitoring applications. A crucial benefit of the through-transmission methods is that they enable the prediction of Lamb wave attenuation in combination with software like Disperse (ver. 2.0.20a, Imperial College, London, UK, 2013). [ABSTRACT FROM AUTHOR]

The aim of this work is to propose a numerical methodology based on the finite element (FE) method to study the propagation mechanisms of transmitted, converted, and reflected guided waves in stiffened fiber‐reinforced polymeric (FRP) composite structures. The dispersion curves of such modes can help designers to improve the damage detection sensitivity of Lamb waves based structural health monitoring (SHM) systems. Using a FE model, whose accuracy is demonstrated against semi‐analytical data in a previous work, the phenomena of scattering and mode conversion in composite panels characterized by a structural discontinuity, such as a stiffener, are investigated. In particular, the behavior of guided waves is analyzed using stiffeners of different shapes, C and Omega shaped. The research activity makes it possible to highlight conversion of modes and reflected waves at stiffeners, providing new observations compared to the state of the art. The morphological complexities of the structure (thickness steps, multiple boundaries, stiffeners) can be used to redirect guided waves and to allow monitoring of a specific area. Wave reflections interacting with stiffeners, therefore, can be analyzed to improve diagnostics or design new detection strategies. [ABSTRACT FROM AUTHOR]

Purpose: Lamb wave is widely used in damage detection and structural health monitoring of plate-like structures. In the current stage, the propagation of non-axisymmetric circular crested Lamb has not been theoretically solved. Therefore, it is essential to find its exact solutions. Methods: The displacement expressions and frequency equations are derived through three-dimensional elastic theory using Helmholtz conversion. Numerical simulations and experiments are conducted on plates for verification of theoretical results. Results: Both axisymmetric and non-axisymmetric waves are obtained from the governing equation with free boundary conditions. Lamb wave travels along the radial direction and holds as a standing wave in circumferential and longitudinal directions. The displacement field is expressed with Bessel functions in the radial direction and trigonometric functions in circumferential and longitudinal directions. Two families of Lamb wave modes are generated, which are identified by radial wavenumber and circumferential order. The amplitudes of displacements decrease with the propagation distance increases, and the circumferential distributions of displacements are affected by the circumferential order. Results obtained from simulation and experiment provide good verification for theoretical predictions. Conclusions: The analytic solutions are presented in non-axisymmetric problems for circular crested Lamb wave in this paper, which provides a theoretical foundation for further investigation of Lamb wave propagation in plates. [ABSTRACT FROM AUTHOR]

The use of adhesive joints has become increasingly popular in various industries due to their many benefits, such as low weight and good mechanical performance. However, adhesive joints can suffer from defects, one of them being weak adhesion. This defect poses a significant risk to structural integrity and can lead to premature failure, but is hard to detect using existing nondestructive testing methods. Therefore, there is a need for an effective technique that can detect weak adhesion in single-lap joints (SLJ) to prevent failure and assist in maintenance, namely in the framework of structural health monitoring. This paper presents a novel approach utilizing machine learning and Lamb Waves (LW) to determine the level of weak adhesion. Firstly, a numerical model of SLJs with different levels of weak adhesion is created and an original approach is proposed for its validation with data from real samples so that reliable LW data can further be easily generated to train and test any other data-driven algorithm for tackling damage. Secondly, a damage detection method is proposed, based on artificial neural networks and fed with simulated data, to determine the level of damage in SLJs, independent of their location. The results show that the simulation model can be validated with a small set of experimental data, being capable of replicating real damage in SLJs. Additionally, the use of simulated data in the training algorithm can increase the accuracy of the simulation model up to 26% when compared to only considering experimental data. The adopted artificial neural network for detecting weak adhesion emerges as a promising approach, yielding a precision of over 95%. Thus, machine learning and LW data can be used to improve the reliability and accuracy of adhesive bonding quality control, as well function as a technique for structural health monitoring, which can enhance the safety and durability of bonded structures. [ABSTRACT FROM AUTHOR]

In this work, a novel approach of guided wave-based damage identification in composite laminates is proposed. The novelty of this research lies in the implementation of ConvLSTM-based autoencoders for the generation of full wavefield data of propagating guided waves in composite structures. The developed surrogate deep learning model takes as input full wavefield frames of propagating waves in a healthy plate, along with a binary image representing delamination, and predicts the frames of propagating waves in a plate, which contains single delamination. The evaluation of the surrogate model is ultrafast (less than 1 s). Therefore, unlike traditional forward solvers, the surrogate model can be employed efficiently in the inverse framework of damage identification. In this work, particle swarm optimisation is applied as a suitable tool to this end. The proposed method was tested on a synthetic dataset, thus showing that it is capable of estimating the delamination location and size with good accuracy. The test involved full wavefield data in the objective function of the inverse method, but it should be underlined as well that partial data with measurements can be implemented. This is extremely important for practical applications in structural health monitoring where only signals at a finite number of locations are available.

High-speed trains may be subjected to various forms of physical impacts during long-term operation, causing structural damage and endangering driving safety. Therefore, impact damage monitoring remains a daunting challenge for the stable operation of high-speed train structures. The existing methods cannot simultaneously detect the location and severity of impact damage, which poses challenges to structural integrity assessment and preventive maintenance. This article proposes an impact damage monitoring method based on multi-task 2D-CNN. Sensor data fusion is performed using a 2D image processing method to convert a 1D impact damage signal into a 2D grayscale image. The fused grayscale image contains information related to the location and severity of impact damage. A damage detection framework was established using multi-task 2D-CNN for feature extraction, impact location classification, and impact energy quantification. This model can learn the commonalities and characteristics of each task by sharing network structure and parameters and can effectively improve the accuracy of each task. Compared with single-task learning, multi-task learning performs better on the metrics of the impact location task recognizing the impact energy task and reduces the training time by 30.83%. With a reduced number of samples, the performance of multi-task learning is more stable and can still effectively identify the location and severity of impact damage. [ABSTRACT FROM AUTHOR]

Lamb wave is a promising tool for structural health monitoring and nondestructive testing. During propagation, dispersion and multi-mode characteristics generally result in overlapping of wave packets. Sparse decomposition is developed to address the overlap problem. Traditional dictionary design in sparse decomposition is to search optimal matching between dictionary atoms and propagating wave packets. However, design accuracy is limited in broadband Lamb wave because propagating wave packet is difficult to predict with severe amplitude modulation and velocity dispersion. To overcome this limitation, broadband Lamb wave sparse decomposition is proposed. Firstly, tone burst models are used as dictionary atoms to match waveform segment rather than global wave packet. Subsequently, solved sparse vector by basis pursuit algorithm is reshaped into a time-frequency matrix. Finally, non-zero values in the matrix are extracted for reconstruction of separated wave packets. Experiments are given to demonstrate the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

The Lamb wave time-reversal method (TRM) has been widely proposed as a baseline-free technique for Structural health monitoring (SHM) of plate/shell-type structures. Still, its efficacy has never been tested under a variable environment. The present study examines the conventional TRM and establishes that it is unsuitable for baseline-free detection and sizing of damage under varying temperatures since the damage index (DI) is prone to change with temperature rise and generally has low sensitivity to damage. The investigation is performed for block mass-type and notch-type defects using experiments and numerical simulations. The study further tests the refined time-reversal method (RTRM), presented recently, against the same objective. This technique uses Lamb waves of the best reconstruction frequency to probe the structure, at which the reconstructed signal in a healthy plate undergoes minimum deviation from the input signal. It considers an extended wave packet for computing the DI instead of using the main mode alone. We show that this process leads to the lowest DI for undamaged plates and the highest sensitivity to damage. Also, the DI remains almost unchanged during temperature change, irrespective of the damage type, size, and plate thickness. It makes the RTRM very effective for baseline-free identification and sizing of damage under varying temperatures. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) techniques have an important role in the performance of mechanical structures. In particular, piezoelectric transducers (PZT) have been employed for establishing damage detection processes in SHM systems. However, despite their wide use, there is limited information in the literature regarding important characteristics for SHM applications, such as the shape, dimensions and frequencies to use, which can affect the performance of the system used to detect the damage. This article investigates longitudinal and flexural waves for applications using circular piezoelectric transducers bonded to thin plates. The methodology allows one to obtain optimal frequencies to create and capture both types of waves and understand the influence of the geometric characteristics of these transducers on the damage detection. Numerical simulations are carried out to show that a change in the parameters of the piezoelectric transducers can maximize the waves amplitudes incoming at the sensor. Results from experimental tests are presented to demonstrate the proposed methodology. New equations are introduced and they compute output voltage and determine optimal frequencies to monitor the structure. The findings contribute to establishing a more efficient design of a damage detection process involving plate-like structures in SHM systems based on wave propagation. [ABSTRACT FROM AUTHOR]

The ultrasonic guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects in metallic pipelines. The signal processing of guided waves is often challenging due to the complexity of the operational conditions and environment in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for the signal processing and data classification of complex systems, thus showing great potential for damage detection in critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model was utilized for decoding ultrasonic guided waves for damage detection in metallic pipelines, and twenty-nine features were extracted as input to classify different types of defects in metallic pipes. The prediction capacity of the CNN-LSTM model was assessed by comparing it to those of CNN and LSTM. The results demonstrated that the CNN-LSTM hybrid model exhibited much higher accuracy, reaching 94.8%, as compared to CNN and LSTM. Interestingly, the results also revealed that predetermined features, including the time, frequency, and time–frequency domains, could significantly improve the robustness of deep learning approaches, even though deep learning approaches are often believed to include automated feature extraction, without hand-crafted steps as in shallow learning. Furthermore, the CNN-LSTM model displayed higher performance when the noise level was relatively low (e.g., SNR = 9 or higher), as compared to the other two models, but its prediction dropped gradually with the increase of the noise. [ABSTRACT FROM AUTHOR]

Most cracks do not grow straight due to the inhomogeneity of material properties. Furthermore, quantifying structural damage using Lamb waves is difficult due to uncertain crack shape. Here, we present a newly developed finite element model to simulate the propagation of Lamb waves in a cracked plate to quantify the influence of crack size and orientation on the characteristics of Lamb wave propagation. This model includes a piezoelectric actuator and sensor to generate and receive the tone-burst signals of Lamb wave. The damping coefficient of the finite element model was calibrated with the experimental data of plates without a crack. The Lamb wave propagation simulations were performed with various crack sizes, crack orientations, and distances between the actuator and sensor. Our model was utilized to investigate the influence of crack orientation on damage quantification based on the signal phase change and normalized amplitude. We successfully evaluated the effect of the incident angle of the crack, crack size, and distance between the sensors on the change of the plate wave signal characteristics. The signal characteristics obtained from our novel finite element model can be used to develop a quantification model to estimate the crack size, direction, and distance from the sensor. [ABSTRACT FROM AUTHOR]

Crack imaging is attractive in the community of nondestructive testing and structural health monitoring of plate-like structures. However, most existing research focuses on the imaging of single crack, while limited work has been reported for the imaging of multiple cracks. To this end, the application of the Lamb wave-based technique is presented in this paper to image the multiple cracks. The antisymmetric mode (A0) of Lamb wave propagation in the thin plate is simulated using the numerical finite element method, and a circular piezoelectric sensor array is considered to capture the Lamb wave signals. The damage index taking into account the attenuation of the reflected signal amplitude is proposed using the reflection signals from the cracks, and the Hilbert transform is used to extract the information of damage. The damage scanning map is constructed to show the damage location in the plate. The proposed damage identification algorithm based on Lamb wave propagation is also used to detect two cracks in the thin plates. The consideration of wave attenuation improves the identification of multiple cracks. The proposed damage identification algorithm using a phased piezoelectric sensor array shows its potential to locate the cracks in thin plates. [ABSTRACT FROM AUTHOR]

In the field of structural health monitoring, Lamb Wave has become one of the most widely used inspection tools due to its advantages of wide detection range and high sensitivity. In this paper, a new damage detection method for honeycomb sandwich structures based on frequency spectrum and Lamb Wave Tomography is proposed. By means of simulation and experiment, a certain number of sensors were placed on the honeycomb sandwich plate to stimulate and receive the signals in both undamaged and damaged cases. By Lamb Wave Tomography, the differences of signals before and after damage were compared, and the damage indexes were calculated. Furthermore, the probability of each sensor path containing damage was analyzed, and the damage image was finally realized. The technology does not require analysis of the complex multimode propagation properties of Lamb Wave, nor does it require understanding and modeling of the properties of materials or structures. In both simulation and experiment, the localization errors of the damage conform to the detection requirements, thus verifying that the method has certain feasibility in damage detection. [ABSTRACT FROM AUTHOR]

Composite structures are widely used for aerospace applications but are prone to barely visible impact damage from low velocity impacts. Guided wave measurements using sparse arrays of distributed sensors provide an important structural health monitoring (SHM) tool for detecting and localizing impact damage in composites. However, the anisotropy of composites needs to be considered as it can affect guided wave propagation and scattering, impacting imaging performance. Improved defect characterization can be achieved by considering the scattering characteristics for the signal processing. Scattering around two different damage types for multiple incident wave directions in a quasi-isotropic carbon fiber reinforced polymer (CFRP) panel were investigated. Full 3D Finite Element (FE) simulations were compared to the measured scattered guided wave field at an artificial insert delamination. Permanent magnets mounted on an undamaged region of the plate were used as scattering targets and both numerical and experimental scattering patterns were compared to the delamination results. Strong directional dependency was observed for both damage types, with energy focusing along the fiber directions of the outer ply layers. For the delamination, mostly forward scattering is observed for all incident wave directions, whereas the magnet blocked forward wave transmission and scattered wave energy in all directions. 2D scattering matrices were calculated, demonstrating distinct scattering behavior for each damage type. Implications of anisotropy and angular scattering on SHM guided wave sparse array imaging are discussed. [ABSTRACT FROM AUTHOR]

Driven by the rapid advancement in manufacturing technologies, engineering structures with complex geometries are increasingly applied in various industries, posing challenges to the applicability and adaptability of existing structural health monitoring methods based on guided ultrasonic waves. To fulfill the characterization of defects in complex structures, a novel approach featuring a conjunction of zero-group-velocity (ZGV) Lamb waves and polarized poly(vinylidenefluoride-co-trifluoroethylene) (PVDF-TrFE) transducers is proposed. In this approach, the PVDF-TrFE solvent is deposited and in situ polarized on the structure surface to form thin and flexible coatings, with which the ZGV waves can be excited efficiently and measured reliably. On this basis, the defect can be characterized by investigating the defect-induced alteration in ZGV wave features. In experimental validations, disbond defects in multilayer bonded structures are evaluated using the ZGV waves measured with fabricated PVDF-TrFE transducers. For the first time, the ZGV waves are measured in a contact and in situ manner. Compared with conventional noncontact measurement of ZGV waves, the proposed approach features a remarkably improved reliability, convenience for narrowband excitation, immunity to measurement uncertainty and capability of in situ monitoring. The proposed approach can advance the ZGV wave-based methods toward the in situ health monitoring and enable the defect evaluation in emerging complex structures. [ABSTRACT FROM AUTHOR]

A flexoelectric actuator represents a new approach for exciting Lamb waves in plate structures and, thus, can be potentially used for active structural health monitoring systems and microsensors. This paper analytically investigated the performance of a flexoelectric actuator for Lamb wave excitation. The constitutive equation for a thin plate flexoelectric structure was derived using the thermodynamics theory. A significant size effect could be demonstrated by the constitutive equation, which suggests a greatly enhanced curvature excitation performance at a small dimension. The interaction between the ideally bonded flexoelectric actuator with the plate could be simplified into two pairs of pin force and pin moment under the assumption of a weak coupling condition. The strain and the displacement responses to the pin-force and pin-moment pairs were obtained using the integral transform techniques and residue calculus. The pin-moment component mainly yielded an antisymmetric Lamb wave mode, while the pin force was reminiscent of its piezoelectric counterpart. The tuning of either a symmetric or an antisymmetric mode could be achieved by choosing an appropriate excitation frequency. The response under a five-cycle Hanning-window toneburst excitation signal was compared with the finite element analysis results and good agreement between them was found. [ABSTRACT FROM AUTHOR]

Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure’s health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT). layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency.

The early detection of small cracks in large metal structures is a crucial requirement for the implementation of a structural health monitoring (SHM) system with a low transducers density. This work tackles the challenging problem of the early detection of submillimeter notch-type defects with a semielliptical shape and a groove at a constant width of 100 µm and 3 mm depth in a 4.1 mm thick aluminum plate. This defect is investigated with an ultrasonic guided wave (UGW) A0 mode at 550 kHz to investigate the long range in thick metal plates. The mode selection is obtained by interdigital transducers (IDTs) designed to operate with a 5 mm central wavelength. The novel contribution is the validation of the detection by pulse-echo and pitch and catch with UGW transducers to cover a distance up to 70 cm to reduce the transducers density. The analysis of scattering from this submillimeter defect at different orientations is carried out using simulations with a Finite Element Model (FEM). The detection of the defect is obtained by comparing the scattered signals from the defect with baseline signals of the pristine laminate. Finally, the paper shows that the simulated results are in good agreement with the experimental ones, demonstrating the possible implementation in an SHM system based on the efficient propagation of an antisymmetric mode by IDTs.

In Structural Health Monitoring (SHM) applications, the Direction of Arrival (DoA) estimation of Guided Waves (GW) on sensor arrays is often used as a fundamental means to locate Acoustic Sources (AS) generated by damages growth or undesired impacts in thin-wall structures (e.g., plates or shells). In this paper, we consider the problem of designing the arrangement and shape of piezo-sensors in planar clusters in order to optimize the DoA estimation performance in noise-affected measurements. We assume that: (i) the wave propagation velocity is unknown, (ii) the DoA is estimated via the time delays of wavefronts between sensors, and (iii) the maximum value of the time delays is limited. The optimality criterion is derived basing on the Theory of Measurements. The sensor array design is so that the DoA variance is minimized in an average sense by exploiting the Calculus of Variations. In this way, considering a three-sensor cluster and a monitored angles sector of 90°, the optimal time delays–DoA relations are derived. A suitable re-shaping procedure is used to impose such relations and, at the same time, to induce the same spatial filtering effect between sensors so that the sensor acquired signals are equal except for a time-shift. In order to achieve the last aim, the sensors shape is realized by exploiting a technique called Error Diffusion, which is able to emulate piezo-load functions with continuously modulated values. In this way, the Shaped Sensors Optimal Cluster (SS-OC) is derived. A numerical assessment via Green's functions simulations shows improved performance in DoA estimation by means of the SS-OC when compared to clusters realized with conventional piezo-disk transducers. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) systems have shown the potential to detect damage in various types of structures, thus increasing safety and reducing maintenance costs. Among the damage detection methods are those that use small piezoelectric transducers attached to the monitored structure, such as the electromechanical impedance (EMI) and Lamb waves methods. Although both methods have shown to be effective in detecting structural damage, external disturbances such as noise can alter the signals from piezoelectric sensors in real applications and prejudice the correct diagnosis of the monitored structure. Therefore, this paper presents a comparative analysis of the performance of EMI and Lamb waves methods in noisy environments, which is a condition commonly found in real structures. The obtained results add value to the SHM field by allowing to know the tradeoff in choosing the appropriate method regarding the detection, quantification, and location of structural damage under noise conditions. Experimental tests were performed on aluminum structures to assess the effectiveness of both techniques to detect, quantify, and locate structural damage under noise of different intensities. The experimental results show that each technique has different characteristics in terms of effectiveness in detecting, quantifying, and locating damage under noise effects. [ABSTRACT FROM AUTHOR]

Guided wave-based nondestructive testing and structural health monitoring methods have been developed to exhibit attractive potentials and best prospects for rapid and sensitive detection of defects or damage in engineering structures. Different modes of guided waves can provide different sensitivities of damage detection. However, the multimode and mode conversion nature of guided waves poses significant challenges to mode purification of received signals. This study aims to design a metamaterial-based smart transducer for mode purification of Lamb waves in a plate, which can filter out an undesired mode of the Lamb wave to enhance sensing and actuating signals of a dominated mode. The smart transducer consists of a periodic array of shunted piezoelectric unimorphs with staggered polarization directions and is bonded on the surface of a host plate. Numerical and experimental results show that a local resonance bandgap for an anti-symmetric Lamb wave, rather than a symmetric Lamb wave, can be obtained and tuned through the shunting inductance circuit. Within such mode bandgap, the wave control for propagating a specific mode of the Lamb wave can be further realized, i.e., the mode of the Lamb wave is purified. The design presented herein offers enhanced capabilities in controlling guided wave propagation for engineering applications and nondestructive testing techniques. [ABSTRACT FROM AUTHOR]

The establishment of a structural health monitoring (SHM) system for the damage and defects of composite structures is of great theoretical and engineering value to ensure their production and operational safety. Advanced machine learning technologies, such as deep learning, have become one of the main driving forces for state monitoring and predictive analysis of these structures. However, it is difficult to obtain sufficient data to train the deep learning model, which may fail to build an accurate and efficient SHM model. To overcome this problem, a new method based on Lamb waves and the diffusion model (DM) is proposed to realize the identification and classification of different defects for carbon-fiber-reinforced polymer (CFRP) structures. In this study, DM is used as the generation model of data enhancement, and the optimized and improved DDPM model is constructed in this experiment. The deep residual neural network (DenseNet) is used to identify and classify the defect features from the Lamb wave signals. Experimental and test results show that the deep learning framework designed in this study based on DenseNet classification and DDPM data enhancement can accurately detect and classify damage signals of common defects in CFRP composite plates. [ABSTRACT FROM AUTHOR]

Damage detection and localization based on ultrasonic guided waves revealed to be promising for structural health monitoring and nondestructive testing. However, the use of a piezoelectric sensor's network to locate and image damaged areas in composite structures requires a number of precautions including the consideration of anisotropy and baseline signals. The lack of information related to these two parameters drastically deteriorates the imaging performance of numerous signal processing methods. To avoid such deterioration, the present contribution proposes different methods to build baseline signals in different types of composites. Baseline signals are first constructed from a numerical simulation model using the previously determined elasticity tensor of the structure. Since the latter tensor is not always easy to obtain especially in the case of anisotropic materials, a second PZT network is used in order to obtain signals related to Lamb waves propagating in different directions. Waveforms are then translated according to a simplified theoretical propagation model of Lamb waves in homogeneous structures. The application of the different methods on transversely isotropic, unidirectional and quasi-transversely isotropic composites allows to have satisfactory images that well represent the damaged areas with the help of the delay-and-sum algorithm. [ABSTRACT FROM AUTHOR]

Guided acoustic waves (GAW) have proven to be a useful tool for structural health monitoring (SHM). However, the dispersive nature of commonly used Lamb waves compromises the spatial resolution making it difficult to detect small or weakly reflective defects. Here we demonstrate an approach that can compensate for the dispersive effects, allowing advanced algorithms to be used with significantly higher signal-to-noise ratio and spatial resolution. In this paper, the sign coherence factor (SCF) extension of the total focusing method (TFM) algorithm is used. The effectiveness is examined by numerical simulation and experimentally demonstrated by detecting weakly reflective layers with a highly dispersive A0 mode on an aluminum plate, which are not detectable without compensating for the dispersion effects. [ABSTRACT FROM AUTHOR]

Large-scale plate-like structural components have been widely used in engineering fields. The occurrence and accumulation of structural damage will threaten the integrity of plate-like structures. Guided wave-based structural health monitoring (SHM) technology has been demonstrated as an effective tool to detect small-scale damage at a high sensitivity. Due to the advantages of baseline-free, concentrated sensor arrays and rapid inspection of the full area, Lamb wave-phased array SHM systems have been extensively investigated in recent years. However, the inherent multi-mode characteristic of Lamb waves is well-known to have a significant influence on wave propagation and further development of phased array method. In this study, a single Lamb wave mode extraction method combined with phased array SHM system is proposed based on a concentric piezoelectric active transducer (CPAT). First, a brief review of a Lamb wave-phased array system is presented, including working principle, sensor array, and Lamb wave excitation and sensing. Then, the single mode wave extraction method is performed based on the CPAT. The effectiveness of the single mode wave extraction-based phased array SHM system is first examined numerically. Finally, the proposed phased array system is used to monitor large plate-like structures and the availability and validity of the proposed SHM system is confirmed by an experimental study. This work provides an effective and practical tool to detect damage in large-scale plate-like structures. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) strategies based on ultrasonic guided waves are very promising regarding thin-walled lightweight structures. To study the performance of such systems, validated numerical analysis tools have to be used. For that procedure, a benchmark between two numerical methods will be presented. The first promising approach is the elastodynamic finite integration technique (EFIT). Miscellaneous research shows that its capability of capturing wave characteristics and interactions is advanced in various media and structures, including thin-walled composites. The second approach employs conventional shell-type finite elements following the Reissner–Mindlin theory for modelling layered composite structures. The advantage of using such finite element methods (FEM) is their high availability in general purpose simulation tools. As a reference model, the measurement data coming from the Open Guided Waves Project (OGW) was taken into account. The OGW dataset provides the experimental data of ultrasonic guided wave propagation in carbon fiber composite plates with an additional omega stringer. By using this contribution, this experiment was reproduced by simulation. The paper presents the results of a validation and motivates further research, such as in research related to the probability of detection analysis and numerical performance. [ABSTRACT FROM AUTHOR]

Owing to their high sensitivity to material microstructural defects, nonlinear guided waves (NGWs) show great promise for the early detection of incipient damages, conducive to numerous structural health monitoring (SHM) applications. In an SHM system, however, inevitable non-damage-related nonlinear sources exist, which may overwhelm the damage-induced nonlinear components and in turn jeopardize the practical efficacy of the detection methodology. Therefore, these deceptive nonlinear interferences need to be mitigated or eliminated as much as practically possible. By embracing the concept of metamaterials, a wave filtering device, referred to as a meta-filter (MF), is developed in this paper. The MF is constructed through mounting periodic stubs on the surface of the structure under inspection to condition the probing signals under the second harmonic Lamb-wave-based SHM paradigm. Through topological optimization, the MF enables ultra-wide stop bands to eliminate the second harmonic Lamb waves of the probing waves while preserving their strong fundamental wave components. The band structure and the underlying mechanism, alongside the MF-enabled wave properties, are numerically investigated. Upon tactically introducing deceptive nonlinear sources such as the nonlinear adhesive bonding layers in a piezoelectric transducer-activated SHM system, the performance of the MF is examined from the SHM perspective, which is finally validated experimentally using a metal specimen containing local plasticized incipient damage. Results demonstrate that the designed MF allows for flexible selection of the excitation frequency on one hand, and entails significant enhancement of the detection ability of the NGW-based SHM system on the other hand owing to the customized band features arising from the topological optimization. [ABSTRACT FROM AUTHOR]