The development of a Structural Health Monitoring (SHM) system based on ultrasound guided waves often relies on numerical models to determine the appropriate placement or number of sensors or study influential parameters, as an alternative to numerous experiments. The accurate and fast modeling of both actuator/sensor response and wave propagation is thus essential. This paper focuses on efficiently modeling a PZT actuator embedded in a laminated composite plate for guided wave SHM, since such actuation has been scarcely studied so far. Firstly, a multiphysics finite element model is conceived and validated through comparison with experiments. Secondly, a potential simplification of the model based on the well-known pin-force model is studied but fails to represent the actual stress source in the particular case of an embedded PZT actuator. Therefore, a hybrid approach is proposed. It consists of first computing the effective loading induced by an embedded PZT into a composite plate using the multiphysics model and then applying this loading in a purely mechanical model. The features of the guided wavefield simulated with this approach are in good agreement with those simulated with the multiphysics one at low frequencies. Limitations of the approach for higher frequencies are discussed.

In guided wave (GW)-based structural health monitoring (SHM), ultrasonic elastic waves are used to detect damages in structures by comparing the acquired signals with those acquired before defect formation. Making the SHM system automatic, especially for similar structures, such as turbine blades, is rather challenging. The high sensitivity of GWs to environmental and operational conditions, the variabilities due to sensor positioning, sensor coupling, and material variability in composites limit the baseline application. This work presents a machine learning (ML)-based damage detection method using aggregated baselines independent of their damaged states to enhance the generalization capability of ML algorithms by considering similar structures' variabilities. The methodology relies on feature extraction from raw GW signals and training classification algorithms (e.g., kernel machines, ensemble methods, and neural networks). Two experimental data sets on composite panels are used. The first experimental data set of 45 composite panels is used to validate the approach by considering the aforementioned inter-specimen variabilities. Half of the 45 panels provide pristine data, and the rest provide damaged data so that the same sample is present in the training or test set but never in both. High classification performance is obtained, demonstrating that the classifier has successfully learned to recognize defect signatures despite the influence of the variabilities linked to the multiple instrumented specimens. The second experimental data set of 1 composite panel with temperature variation is used. Good classification performance is obtained without using baseline correction methods.

Simulation has been recognized as a promising option to reduce the time and costs associated with determining probability of detection curves to demonstrate the performance of guided wave-based structural health monitoring (GW-SHM) systems. Time-domain transient spectral finite element schemes have been used for large GW-SHM simulation campaigns, but the most common piezoelectric transducer model used for actuation, the pin force model, has limitations in terms of its range of validity. This is because the excitation frequency for the pin force model has only been validated far below the first electromechanical resonance frequency of the piezoelectric transducer mainly due to not considering the normal stress and dynamics of the transducer. As a result, the value of simulation tools for performance demonstrations may be limited. To address this limitation, this paper introduces a hybrid actuator model that integrates frequency-dependent complex interfacial stresses in both the shear and normal directions, computed using finite elements. These surface stresses are compatible with time-domain transient spectral finite element schemes, enabling their seamless integration without compromising the required performance for conducting intensive simulation campaigns. The proposed hybrid actuator model undergoes validation through a combination of simulation and experimental studies. Additionally, a comprehensive parametric study is conducted to assess the model’s validity across a wide range of excitation frequencies. The results demonstrate the accurate representation of the transduction signal above the piezoelectric transducer’s first free electromechanical resonance frequency.

In the context of an embedded structural health monitoring (SHM) system, two methods of transducer integration into the core of a laminate carbon fiber-reinforced polymer (CFRP) are tested: cut-out and between two plies. This study focuses on the effect of integration methods on Lamb wave generation. For this purpose, plates with an embedded lead zirconate titanate (PZT) transducer are cured in an autoclave. The embedded PZT insulation, integrity, and ability to generate Lamb waves are checked with electromechanical impedance, X-rays, and laser Doppler vibrometry (LDV) measurements. Lamb wave dispersion curves are computed by LDV using two-dimensional fast Fourier transform (Bi-FFT) to study the quasi-antisymmetric mode (qA0) excitability in generation with the embedded PZT in the frequency range of 30 to 200 kHz. The embedded PZT is able to generate Lamb waves, which validate the integration procedure. The first minimum frequency of the embedded PZT shifts to lower frequencies and its amplitude is reduced compared to a surface-mounted PZT.

SHM systems using elastic guided waves are an attractive solution for the monitoring of structures. The development of a robust methodology to demonstrate the performances of such systems in terms of probability of detection motivates efforts of the scientific community. In this communication we present a Model Assisted POD study of the detection of growing cracks in laboratory samples using powerful simulation capabilities and applying recent statistical methods proposed in the litterature. The MAPOD estimation is validated by comparison with experimental trials.

The use of a reference state, i.e. a baseline, for data analysis in guided wave based structural health monitoring is limiting the range of applicability of such approaches. This has been largely demonstrated in the literature in the presence of temperature changes but is also true in the presence of other varying environmental and operational conditions, as well as aging effects. This paper presents a novel self-referenced methodology, which is built to be intrinsically robust to the influence of external effects, allegedly including the ones not identified during system design. The method relies on the concept of instantaneous baseline, i.e. the comparison of multiple measurements acquired on structures with a high degree of similarity from a guided wave perspective. The subtlety of the approach is the detection of flaws of small influence on the measurements in the presence of extrinsic and intrinsic variabilities of comparatively larger influence over several similar samples. The method also includes a dimension reduction through the extraction of quantities of interest from the acquired signals, potentially enabling the analysis of several samples with limited data volume.The proposed method is successfully validated on 12 aluminum plates, each with a through-hole crack from 1 to 30 mm in length in a laboratory environment with limited external parameters. Next, the approach is tested on 2 woven composite samples of complex shape with up to 7 similar paths from a guided wave perspective. The detection of an added mass is successful over the whole temperature range under consideration (-30 degrees C to 30 degrees C) for all studied interrogating frequencies (50, 100, and 150 kHz). The proposed methodology performs significantly better than a state of the art imaging algorithm with temperature compensation applied to the same data.

L’utilisation d’un état de référence, c’est-à-dire d’une baseline, pour l’analyse des données dans le cadre du contrôle santé des intégré (Structural Health Monitoring, SHM) par ondes guidées limite le champ d’application de ces approches. Cela a largement été démontré dans la littérature en présence de changement de température, mais c’est également vrai en présence de variations d’autres conditions environnementales et opérationnelles, ainsi que des effets du vieillissement. Cet article présente une nouvelle méthodologie auto-référencée, conçue pour être intrinsèquement robuste à l’influence des effets externes, y compris ceux qui n’ont pas été identifiés lors de la conception du système. La méthode repose sur le concept de baseline instantanée, c’est-à-dire la comparaison de plusieurs mesures acquises sur des mesures présentant un degré élevé de similitude du point de vue des ondes guidées. La subtilité de l'approche réside dans la détection de défauts ayant une faible influence sur les mesures en présence de variabilités extrinsèques et intrinsèques ayant une influence comparativement plus importante sur plusieurs échantillons similaires. La méthode comprend également une réduction de la dimension par l'extraction de quantités d'intérêt à partir des signaux acquis, permettant potentiellement l'analyse de plusieurs échantillons avec un volume de données limitées. La méthode proposée est validée avec succès sur 12 plaques d'aluminium, chacune présentant une fissure traversante de 1 à 30mm de longueur, dans un environnement de laboratoire avec des paramètres externes limités. Ensuite, l'approche est testée sur 2 échantillons composites tissés de forme complexe avec jusqu'à 7 trajectoires similaires du point de vue des ondes guidées. La détection d'une masse ajoutée est réussie sur toute la plage de températures considérée (-30◦C à 30◦C) pour toutes les fréquences d'interrogation étudiées (50, 100 et 150 kHz). La méthodologie proposée est nettement plus performante qu'un algorithme d'imagerie de l’état de l’art avec compensation de température appliqué aux mêmes données.

L’utilisation des méthodes de contrôle nondestructif (CND) est essentielle pour assurer l’intégrité des structures aéronautiques, et les systèmes de contrôle santé intégré des structures (SHM) offrent la possibilité de surveiller en temps réel l’état des structures ou de contrôler des pièces difficiles d’accès. Les matériaux composites, de plus en plus utilisés pour la fabrication des aéronefs, permettent d’intégrer des systèmes SHM directement à coeur du matériau. L’étude se concentre sur l’intégration d’un transducteur piézoélectrique (PZT) à coeur du composite stratifié. L’intégrité du PZT intégré est vérifiée par radiographie X, et sa capacité d’émission et de réception des ondes ultrasonores guidées est vérifiée avec un banc de vibrométrie laser et des essais pitch-catch. Les résultats montrent que l'intégration du PZT à coeur du composite est réussie, et que le système SHM intégré est capable de générer et détecter des ondes ultrasonores guidées. Les paramètres optimaux pour réaliser un essai de détection de défauts sont ensuite déterminés. L’amplitude des ondes de flexion est favorisée par un positionnement du PZT intégré près de la surface du composite. De plus, la fréquence d'excitation optimale du PZT est déterminée pour permettre une détection efficace des défauts. Les résultats ouvrent la voie à l'utilisation de cette méthode pour assurer la surveillance et la maintenance des structures aéronautiques de manière fiable et plus efficace. 1.

Guided wave-based structural health monitoring (GW-SHM) relies on permanently installed sensors to detect and monitor structural defects such as cracks or delamination. Millimeter to centimeter-sized defects are detected over areas of several square meters with sparse sensor networks installed on metallic or composite structures. So far, the main drivers of the development of such technologies have been expectations of cost savings and/or an increased safety. As monitoring requires a complex cyber physical system including, at least an energy source, sensors, data acquisition, treatment, and communication capabilities, adding monitoring capabilities to a structure leads to an intrinsic environmental impact. This paper targets for the first time the environmental assessment of GW-SHM. Such analysis being use-case dependent, two prospective applications are studied: railroad monitoring and wind turbine monitoring. A complete GW-SHM system prototype is defined to quantify its environmental impact, and exploitation scenarios are proposed to estimate the potential environmental gains provided by the monitoring. For the scenarios under consideration, the studied system is found to be either carbon neutral or carbon negative (i.e., favorable), but this result is limited to the functional units under consideration. The present study is expected to be a template for further environmental assessments of monitoring systems, which shall be conducted for each prospective application and refined often as systems mature and exploitation scenarios become clearer and exploitation data available.

In the last decades, composite materials have been increasingly used in aircraft structures as a mean to reduce the weight. For safety reasons, periodic checks of impact-induced damage need to be performed. In that framework, ultrasound non destructive testing has proven its ability to detect macro-defects such as delamination. However, at early stages, Barely Visible Impact Damage (BVID) may not be detected via linear ultrasound techniques. In recent years, non-linear ultrasound has gained traction for the inspection of such defects. However, detection of the weak non-linear defect signature buried in ambient noise remains challenging, and depends on an empirical choice of excitation frequencies. This study focuses on the evaluation of a non-linear pump probe ultrasound inspection technique applied to BVID in multilayer carbon fibre reinforced composites used in aeronautics. In particular, the influence of various experimental parameters on the measured non-linear response is studied. A low frequency pump wave (via a shaker) and a high frequency wave (via a piezoelectric transducer) are transmitted to the medium while a second transducer records the ultrasonic response. Both pump and probe waves are sent in the form of a few secondslong frequency sweep, in the audible and ultrasonic range respectively. Appropriate post processing is then applied to the recorded signals to retrieve the non-linear response of the defect. The procedure is repeated on several composite plates with and without impact damages. The nonlinear response appears in the form of modulation sidebands in the frequency spectrum of the post-processed signals. The relative amplitudes of the side bands obtained for the various samples with various defect sizes and excitation amplitude or frequency content are studied. Optimal experimental parameters were obtained and led to a good detection of defects. The limits of the method are also discussed.

In the context of Guided Wave-based Structural Health Monitoring (GW-SHM), ultrasonic elastic waves are used to detect damages in structures by comparing the acquired signals with those from a defect-free structure. However, the high sensitivity of GWs to environmental and operational conditions limits the validity of such references. Notably, variabilities between multiple specimens are often significant from the GWs perspective. These variabilities are particularly important in composites and are due to sensor positioning, sensor coupling and material variability. This communication presents a baseline-free approach using physics-enhanced Machine Learning (ML) for enhanced robustness. To ensure the coverage of these variabilities the approach is validated on multiple Carbon-fiber-reinforced polymers (CFRP) panels. The methodology relies on feature extraction from raw GW signals and training classification algorithms (e.g., kernel machines, neural networks). To make the classifier learn inter-specimen variabilities, an experimental database of 45 impacted composite panels is used. Half of them are used to provide pristine data, and the rest to provide damaged data so that the same sample is either in the training or test set, but never in both. Good classification performance is obtained, demonstrating that the classifier has successfully learnt to recognize defect signatures despite the variability linked to the multiple specimens and instrumentations.

Composites represent approximately 50% of the weight of structural parts in new aircraft as Airbus 350 or Boeing 787. Damages could occur on these parts and their monitoring is required for the safety of users. A Structural Health Monitoring system composed by a Lamb’s waves generator and a sensor is a privileged candidate to detect such damages. The flexibility of composite manufacturing allows the integration of such a system. The topic of this study is to optimize the integration of a Structural Health Monitoring system in aircraft structural parts. The present article focuses on the optimization of the integration method of a piezoelectric Lead Zirconate Titanate transducer into laminated composite (Carbon/Epoxy), cured in autoclave according to aircraft manufacturing requirements. The health state of the integrated transducer with three connecting methods is evaluated using X-ray scanning. Punctual stress is responsible for the crack of the transducer occurred during composite curing. The connecting method with aluminum sheet and silver joint is selected because it minimizes local stresses and keeps the transducer integrity. The cohesion between the integrated transducer and the host material is observed by optical microscopy, it shows the presence of void zones located in the PZT edges. A Laser Doppler Vibrometer scanning shows the ability of the integrated piezoelectric transducer to generate Lamb waves.

In the context of airplane structures monitoring, the performance of an embedded SHM system for composite material components is studied. The present article focuses on the influence of the PZT transducers integration on the Lamb waves it generates. First the article presents finite element modeling of Lamb waves generation in a carbon/epoxy plate, either with surface mounted or with embedded PZT transducer. Ultrasonic wavefield simulated in both configurations are compared. Experiments are also performed on composite laminates with embedded or surface mounted PZT. The out-of-plane displacement induced by PZT excitation on the plate surface is measured by Laser Doppler Vibrometry in both configurations. Out-of-plane displacements are compared together.

Composite-overwrapped pressure vessels (COPV) are increasingly used in the transportation industry due to their high strength to mass ratio. Throughout the years, various designs were developed and found their applications. Currently, there are five designs, which can be subdivided into two main categories - with a load-sharing metal liner and with a non-load-sharing plastic liner. The main damage mechanism defining the lifetime of the first type is fatigue of the metal liner, whereas for the second type it is fatigue of the composite overwrap. Nevertheless, one damage type which may drastically reduce the lifetime of COPV is impact-induced damage. Therefore, this barely visible damage needs to be assessed in a non-destructive way to decide whether the pressure vessel can be further used or has to be put out of service. One of the possible methods is based on ultrasonic waves. In this contribution, both conventional ultrasonic testing (UT) by high-frequency bulk waves and wavenumber mapping by low frequency guided waves are used to evaluate impact damage. Wavenumber mapping techniques are first benchmarked on a simulated aluminium panel then applied to experimental measurements acquired on a delaminated aluminium-CFRP composite plate which corresponds to a structure of COPV with a load-sharing metal liner. The analysis of experimental data obtained from measurements of guided waves propagating in an aluminium-CFRP composite plate with impact-induced damage is performed. All approaches show similar performance in terms of quantification of damage size and depths while being applied to numerical data. The approaches used on the experimental data deliver an accurate estimate of the in-plane size of the large delamination at the aluminium-CFRP interface but only a rough estimate of its depth. Moreover, none of the wavenumber mapping techniques used in the study can quantify every delamination between CFRP plies caused by the impact, which is the case for conventional UT. This may be solved by using higher frequencies (shorter wavelengths) or more advanced signal processing techniques. All in all, it can be concluded that imaging of complex impact damage in fibre-reinforced composites based on wavenumber mapping is not straightforward and stays a challenging task.

A guided wave-based structural health monitoring (GW-SHM) system aims at determining the integrity of a wide variety of plate-like structures such as aircraft fuselages, pipes, and fuel tanks. It is often based on a sparse grid of piezoelectric transducers for exciting and sensing GWs that under certain conditions interact with damage while propagating. In recent years, various defect imaging algorithms have been proposed for processing GWs signals and, particularly, for computing an image representing the integrity of the studied structure. The performance of the GW-SHM system highly depends on a signal processing methodology. This paper compares defect localization accuracy of the three state-of-art defect imaging algorithms (delay-and-sum, minimum variance, and excitelet) applied to an extensive simulated database of GWs propagation and GWs-defect interaction in aluminum plate under varying temperature and transducers degradation. This study is conducted in order to provide statistical inferences, essential for SHM system performance demonstration.

This paper proposes an automatic defect localization and sizing procedure for Structural Health Monitoring based on guided waves imaging. The procedure is applied to an aluminum plate equipped with active piezoelectric sensors. The defect localization and sizing strategy is obtained through to the use of a convolutional neural network trained exclusively on numerical simulations of guided wave signals and post-processed by the delay and sum imaging algorithm. The paper shows the effectiveness of the proposed approach to invert both synthetic and experimental data.