Environmental and operational conditions effects on Lamb wave based structural health monitoring systems: A review

Analytical and experimental analysis of guided waves in an aluminum plate under bending load

Although guided waves offer great potential for monitoring various structures, interpreting signals from piezoelectric sensors remains a challenging task. One main reason is the significant influence of environmental conditions on the wave propagation. A lot of research has already been done on the influence of temperature effects and recently more attention has been shifted towards loads. While previous publications have mainly focused on uni- or bi-directional loads, this publication expands the developed models to include bending loads. After reviewing the analytical basis of acoustoelasticity, the derived equations are expanded to nonhomogeneous elastic bending loads using the partial wave method. The analysis is completed using recent results developed by C. Hakoda and C. J. Lissenden (2018) [1], that gave more physical insight in the propagation of guided waves in various frequency-bands. The focus of the experimental analysis is around the fundamental S0- and A0-Modes of Lamb waves. To validate the analytical results an aluminum plate is instrumented using piezoelectric transducers and loaded with varying bending loads. The experimental results are in good agreement with the analytical theory and demonstrate the influence of bending prestress on guided wave propagation. Based on these results an innovative measurement method for bending loads is developed, that is robust to small temperature changes.

A physics-embedded deep-learning framework for efficient multi-fidelity modeling applied to guided wave based structural health monitoring

Health monitoring of structures using ultrasonic guided waves is an evolving technology with potential applications in monitoring pipelines, civil bridges, and aircraft components. However, the sensitivity of guided waves to external parameters affects the reliability of monitoring systems based on them. These influencing factors and experimental related factors cannot be perfectly modeled, which give rise to the discrepancy between numerical simulations and experimental measurements. Therefore, it is important to address this inevitable discrepancy and generate close-to-experiment simulations. In this work, we present a deep learning-based Digital Twin framework containing multi-fidelity modeling to reduce the discrepancy between measurements and simulations and a deep generative model to generate close-to-experiment guided wave responses by harnessing the vital characteristics of the two sources. These realistic simulations (close to experiment) can then be used in assessing the reliability of health monitoring system by generating probability of detection curves. Furthermore, they can also be used for augmenting the training data for a machine learning algorithm. We use a measurement dataset corresponding to crack propagation and simulations to validate the proposed framework. The results show that the discrepancy is indeed reduced to a great extent, furthermore, we also show that this framework enables the computation of probability of detection from close-to-experiment data as a direct consequence of rapid generation of realistic simulations.

Experimental validation of zero-group-velocity feature guided waves in a welded joint utilizing the pitch-catch measurement technique with air-coupled ultrasonic transducers

Zero-Group-Velocity (ZGV) Lamb waves in elastic plates had been conducted extensive theoretical and experimental researches in the field of ultrasonic nondestructive testing. The ZGV modes in complex structures had been studied theoretically, but less attention had been paid to their experimental investigation. This paper reports the experimental observation of Zero-Group-Velocity Feature Guided Waves (ZGV-FGWs) in a welded joint using the pitch-catch measurement technique with air-coupled ultrasonic transducers. Firstly, for the elastic plate, it is verified that the received time-domain signal using the pitch-catch measurement method with air-coupled ultrasonic transducers is indeed ZGV Lamb waves. Subsequently, we applied the same pitch-catch measurement method with air-coupled ultrasonic transducers to receive time-domain signals at different excitation frequencies in the welded joint. It is observed that the received time-domain signals in the welded joint oscillate for extended periods of time. By performing short-time Fourier transforms on the received time-domain signals, we analyze the frequency content of the received time-domain signals at different excitation carrier frequencies. By analyzing the spectral amplitude variations of these signals at different excitation carrier frequencies, it can be demonstrated that the spectral amplitude corresponding to the resonance frequency is the largest. These findings collectively affirm that the received time-domain signals in the welded joint exhibit ZGV characteristics, identified as ZGV-FGWs. Consequently, from an experimental perspective, the presence of ZGV-FGWs in the welded joint is verified. Moreover, the experimentally determined resonance frequency of ZGV-FGWs concurs with the results obtained through simulation. This study confirms the feasibility of using the pitch-catch measurement method with air-coupled ultrasonic transducers to excite ZGV-FGWs in a welded joint and provides a reference for future experimental investigations of ZGV-FGWs in complex structures.

A damage localization technique using wave front shapes in composite laminates without knowing the velocity profile

Composite laminates are widely used in various fields, but their structures are prone to cracks and damage. Due to the difference in angles of the instantaneous direction of the wave front propagation and the direction of the energy flow in an anisotropic material, the use of Lamb waves for damage localization in composite laminates is a challenging task. Establishing the wave front shape equation can overcome the difficulty of damage localization caused by anisotropy, but this usually requires a priori knowledge of the acoustic velocity distribution of the laminates, which is not convenient for efficient damage localization. In this paper, a damage localization method based on wave front shapes for composite laminates without any knowledge of the velocity profile is presented. Numerical simulation and experimental results show that the proposed method works. This method shows good damage localization accuracy and has broad application prospects in non-destructive testing for plate structures with strong anisotropy.

Particle filter for fatigue crack growth prediction using SH0 wave on-line monitoring

Fatigue crack is one of the main failure modes of pressure vessels. Online monitoring and predicting methods of crack growth play an important role in the operation of important pressure vessel. The SH0 wave is non-dispersive, and it is not disturbed by internal media of pressure vessel and very sensitive to cracks, therefore it is suitable for fatigue crack growth monitoring. Moreover, fatigue crack growth in industry is affected by material properties, loads, which usually shows some uncertainty. And the particle filter (PF) is well suited to deal with prediction problems affected by uncertainty. Hence, the prediction method of crack growth based on SH0 wave monitoring and PF is proposed (short for SH0-PF). The basic theory of crack monitoring method using SH0 wave is introduced, and the signal feature extraction using the damage index is studied. The state equation characterizing the fatigue crack growth is established by Paris model, and the observation equation is established based on the normalized correlation moment damage index according to monitoring signal using SH0 wave. The prediction reliability of the fatigue crack growth applying SH0-PF is verified by experiment with the single edge notched specimen. The experimental results indicate that the prediction accuracy of SH0-PF is better than that of the traditional Paris model.

Energy focusing of broadband Lamb wave by designing excitation waveforms and elastic metamaterials

In the field of structure health monitoring (SHM), the use of Lamb wave to locate damage is a common method. Energy focusing is beneficial for damage localization because of higher SNR and higher resolution. Optimization design of elastic metamaterials is promising for energy focusing based on speed modulation. However, current design scheme is effective only for narrowband Lamb waves. Compared to narrowband waves, broadband Lamb waves with a larger frequency range carry richer structural information. In this study, an energy focusing method based on broadband Lamb waves by simultaneous designing excitation waveforms and elastic metamaterials is proposed. Firstly, COMSOL finite element simulation software is used to calculate the relationship between the metamaterial structure and the excitation wave. Subsequently, the metamaterial structure and the excitation signal form are designed according to the relationship. Finally, the metamaterial structure is bonded with the aluminum plate using 3D printing PA2200 nylon to verify the effectiveness of the method.

Experimental demonstration of rainbow trapping of elastic waves in two-dimensional axisymmetric phononic crystal platesa)

Structures with specific graded geometries or properties can cause spatial separation and local field enhancement of wave energy. This phenomenon is called rainbow trapping, which manifests itself as stopping the propagation of waves at different locations according to their frequencies. In acoustics, most research on rainbow trapping has focused on wave propagation in one dimension. This research examined the elastic wave trapping performance of a two-dimensional (2D) axisymmetric grooved phononic crystal plate structure. The performance of the proposed structure is validated using numerical simulations based on finite element analysis and experimental measurements using a laser Doppler vibrometer. It is found that rainbow trapping within the frequency range of 165-205 kHz is achieved, where elastic waves are trapped at different radial distances in the plate. The results demonstrate that the proposed design is capable of effectively capturing elastic waves across a broad frequency range of interest. This concept could be useful in applications such as filtering and energy harvesting by concentrating wave energy at different locations in the structure.

Mitigating the Impact of Temperature Variations on Ultrasonic Guided Wave-Based Structural Health Monitoring through Variational Autoencoders

Structural health monitoring (SHM) has become paramount for developing cheaper and more reliable maintenance policies. The advantages coming from adopting such process have turned out to be particularly evident when dealing with plated structures. In this context, state-of-the-art methods are based on exciting and acquiring ultrasonic-guided waves through a permanently installed sensor network. A baseline is registered when the structure is healthy, and newly acquired signals are compared to it to detect, localize, and quantify damage. To this purpose, the performance of traditional methods has been overcome by data-driven approaches, which allow processing a larger amount of data without losing diagnostic information. However, to date, no diagnostic method can deal with varying environmental and operational conditions (EOCs). This work aims to present a proof-of-concept that state-of-the-art machine learning methods can be used for reducing the impact of EOCs on the performance of damage diagnosis methods. Generative artificial intelligence was leveraged to mitigate the impact of temperature variations on ultrasonic guided wave-based SHM. Specifically, variational autoencoders and singular value decomposition were combined to learn the influence of temperature on guided waves. After training, the generative part of the algorithm was used to reconstruct signals at new unseen temperatures. Moreover, a refined version of the algorithm called forced variational autoencoder was introduced to further improve the reconstruction capabilities. The accuracy of the proposed framework was demonstrated against real measurements on a composite plate.

Trend Decomposition for Temperature Compensation in a Radar-Based Structural Health Monitoring System of Wind Turbine Blades

The compensation of temperature is critical in every structural health monitoring (SHM) system for achieving maximum damage detection performance. This paper analyses a novel approach based on seasonal trend decomposition to eliminate the temperature effect in a radar-based SHM system for wind turbine blades that operates in the frequency band from 58 to 63.5 GHz. While the original seasonal trend decomposition searches for the trend of a periodic signal in its entirety, the new method uses a moving average to determine trends for each point of a periodic signal. The points of the seasonal signal no longer need to have the same trend. Based on the determined trends, the measurement signal can be corrected by temperature effects, providing accurate damage detection results under changing temperature conditions. The performance of the trend decomposition is demonstrated with experimental data obtained during a full-scale fatigue test of a 31 m long wind turbine blade subjected to ambient temperature variations. For comparison, the well-known optimal baseline selection (OBS) approach is used, which is based on multiple baseline measurements at different temperature conditions. The use of metrics, such as the contrast in damage indicators, enables the performance assessment of both methods.

Methodologies for modeling and identification of breathing crack: A review

Swift development of technology for monitoring complex structures demands major attention on the precision of damage detection methods. The early detection of any type of deterioration or degradation of structures is of paramount importance to avoid sudden catastrophic failure. It warns users about the impending state of the system. At the initiation of a crack or some other system faults, the system may generate a time-varying state of crack under ambient vibration. It represents the nonlinear breathing phenomena of crack. An assessment of this degree of nonlinearity can be utilized for the detection, localization, and quantification of breathing cracks. Appropriate modeling of such cracks is thus necessary to capture distinctive nonlinear features. Recognizing this importance, various methods of modeling and nonlinear system identification which have been employed in the past for the detection of breathing crack are reviewed. The present study also explores some of the available vibration as well as acoustic-based damage identification techniques, chronologically connecting their evolutions. It summarizes the advantages and limitations of the methods to inspect potential future applications. The future scopes drawn from this review are highlighted to pave the path of wide-spread applications of nonlinear features of crack.

Damage Identification of Railway Bridges through Temporal Autoregressive Modeling

The damage identification of railway bridges poses a formidable challenge given the large variability in the environmental and operational conditions that such structures are subjected to along their lifespan. To address this challenge, this paper proposes a novel damage identification approach exploiting continuously extracted time series of autoregressive (AR) coefficients from strain data with moving train loads as highly sensitive damage features. Through a statistical pattern recognition algorithm involving data clustering and quality control charts, the proposed approach offers a set of sensor-level damage indicators with damage detection, quantification, and localization capabilities. The effectiveness of the developed approach is appraised through two case studies, involving a theoretical simply supported beam and a real-world in-operation railway bridge. The latter corresponds to the Mascarat Viaduct, a 20th century historical steel truss railway bridge that remains active in TRAM line 9 in the province of Alicante, Spain. A detailed 3D finite element model (FEM) of the viaduct was defined and experimentally validated. On this basis, an extensive synthetic dataset was constructed accounting for both environmental and operational conditions, as well as a variety of damage scenarios of increasing severity. Overall, the presented results and discussion evidence the superior performance of strain measurements over acceleration, offering great potential for unsupervised damage detection with full damage identification capabilities (detection, quantification, and localization).

Lamb Wave-Based Structural Damage Detection: A Time Series Approach Using Cointegration

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.

Structural Health Monitoring of Chemical Storage Tanks with Application of PZT Sensors

Chemical pressure storage tanks are containers designed to store fluids at high pressures, i.e., their internal pressure is higher than the atmospheric pressure. They can come in various shapes and sizes, and may be fabricated from a variety of materials. As aggressive chemical agents stored under elevated pressures can cause significant damage to both people and the environment, it is essential to develop systems for the early damage detection and the monitoring of structural integrity of such vessels. The development of early damage detection and condition monitoring systems could also help to reduce the maintenance costs associated with periodic inspections of the structure and unforeseen operational breaks due to unmonitored damage development. It could also reduce the related environmental burden. In this paper, we consider a hybrid material composed of glass-fiber-reinforced polymers (GFRPs) and a polyethylene (PE) layer that is suitable for pressurized chemical storage tank manufacturing. GFRPs are used for the outer layer of the tank structure and provides the dominant part of the construction stiffness, while the PE layer is used for protection against the stored chemical medium. The considered damage scenarios include simulated cracks and an erosion of the inner PE layer, as these can be early signs of structural damage leading to the leakage of hazardous liquids, which could compromise safety and, possibly, harm the environment. For damage detection, PZT sensors were selected due to their widely recognized applicability for the purpose of structural health monitoring. For sensor installation, it was assumed that only the outer GFRP layer was available as otherwise sensors could be affected by the stored chemical agent. The main focus of this paper is to verify whether elastic waves excited by PZT sensors, which are installed on the outer GFRP layer, can penetrate the GFRP and PE interface and can be used to detect damage occurring in the inner PE layer. The efficiency of different signal characteristics used for structure evaluation is compared for various frequencies and durations of the excitation signal as well as feasibility of PZT sensor application for passive acquisition of acoustic emission signals is verified.

Constrained thickness-shear vibration-based piezoelectric transducers for generating unidirectional-propagation SH0 wave

Excitation of a pure guided wave with a controllable wavefield is essential in structural health monitoring (SHM). For example, a unidirectional-propagation guided wave can significantly reduce the complexity of signal interpretations by avoiding unwanted reflections. However, few transducers are currently capable of exciting a pure unidirectional-propagation guided wave, which cannot satisfy the emerging demands from the field of SHM. In this work, the thickness-shear vibration characteristics of the piezoelectric PZT wafer bonded on a waveguide are investigated by theoretical modeling and numerical simulations. It is found that there is a phase difference between the electric-excitation signal applied on the PZT wafer and the mechanical response signal of the bottom surface of the viscoelastic adhesive layer that connects the PZT wafer and waveguide. Moreover, such a phase difference can be adjusted by changing the equivalent width of the PZT wafer. Based on this finding, two piezoelectric transducers with different shape configurations are proposed to excite the unidirectional-propagation SH₀ wave (the fundamental shear horizontal wave). Finite element simulations and experiments are conducted to verify the performances of the two unidirectional transducers. Results show that the two transducers can excite a pure SH₀ wave and enhance the wave energy along a single direction. No time delay is required to excite the proposed transducers. Due to their simple configurations, the developed unidirectional SH₀ wave transducers will have great potential applications in SHM.

Elastic wave propagation in thick-walled hollow cylinders for damage localization through inner surface sensing

Thick-walled hollow cylinders (TWHCs) are widely used in engineering structures and transportation systems, exemplified by train axles. The real-time and online health monitoring of such structures is crucial to ensure their structural integrity and operational safety. While elastic-wave-based structural health monitoring (SHM) shows promise, the development of feasible methods strongly relies on a good understanding and exploitation of the wave propagation properties and their interaction with structural defects. TWHCs usually bear multiple wave modes, which is a less investigated and explored topic as compared with thin-walled structures. This work examines this issue and proposes a dedicated damage localization strategy by using the selected waves captured on the inner surface of a TWHC. It is shown that, alongside the quasi-surface-waves on the outer surface, longitudinal waves converted from the thickness-through shear bulk waves are generated to propagate along the inner surface. Their propagation characteristics are exploited for damage localization based on hyperbolic loci methods through inner surface sensing. Numerical studies are conducted to validate the method and assess different transducer configurations, alongside experimental verifications on a benchmark TWHC containing a notch-type defect. Studies provide guidance on damage detection in TWHCs and sensor network design.

A review in guided-ultrasonic-wave-based structural health monitoring: From fundamental theory to machine learning techniques

The development of structural health monitoring (SHM) techniques is of great importance to improve the structural efficiency and safety. With advantages of long propagation distances, high damage sensitivity, and economic feasibility, guided-ultrasonic-wave-based SHM is recognized as one of the most promising technologies for large-scale engineering structures. However, the propagation characteristics of guided ultrasonic waves in in-service engineering structures are highly complex, which results in difficulties in developing precise and efficient signal feature mining methods. The damage identification efficiency and reliability of existing guided ultrasonic wave methods cannot meet engineering requirements. With the development of machine learning (ML), numerous researchers have proposed improved ML methods that can be incorporated into guided ultrasonic wave diagnostic techniques for SHM of actual engineering structures. To highlight their contributions, this paper provides a state-of-the-art overview of the guided-wave-based SHM techniques enabled by ML methods. Accordingly, multiple stages required for ML-based guided ultrasonic wave techniques are discussed, including guided ultrasonic wave propagation modeling, guided ultrasonic wave data acquisition, wave signal pre-processing, guided wave data-based ML modeling, and physics-based ML modeling. By placing ML methods in the context of the guided-wave-based SHM for actual engineering structures, this paper also provides insights into future prospects and research strategies.

Wave Dispersion Behavior in Quasi-Solid State Concrete Hydration

This paper aims to investigate wave dispersion behavior in the quasi-solid state of concrete to better understand microstructure hydration interactions. The quasi-solid state refers to the consistency of the mixture between the initial liquid-solid stage and the hardened stage, where the concrete has not yet fully solidified but still exhibits viscous behavior. The study seeks to enable a more accurate evaluation of the optimal time for the quasi-liquid product of concrete using both contact and noncontact sensors, as current set time measurement approaches based on group velocity may not provide a comprehensive understanding of the hydration phenomenon. To achieve this goal, the wave dispersion behavior of P-wave and surface wave with transducers and sensors is studied. The dispersion behavior with different concrete mixtures and the phase velocity comparison of dispersion behavior are investigated. The analytical solutions are used to validate the measured data. The laboratory test specimen with w/c = 0.5 was subjected to an impulse in a frequency range of 40 kHz to 150 kHz. The results demonstrate that the P-wave results exhibit well-fitted waveform trends with analytical solutions, showing a maximum phase velocity when the impulse frequency is at 50 kHz. The surface wave phase velocity shows distinct patterns at different scanning times, which is attributed to the effect of the microstructure on the wave dispersion behavior. This investigation delivers profound knowledge of hydration and quality control in the quasi-solid state of concrete with wave dispersion behavior, providing a new approach for determining the optimal time of the quasi-liquid product. The criteria and methods developed in this paper can be applied to optimal timing for additive manufacturing of concrete material for 3D printers by utilizing sensors.

Lead Zirconate Titanate Transducers Embedded in Composite Laminates: The Influence of the Integration Method on Ultrasound Transduction

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.

Noninvasive liquid level sensing with laser generated ultrasonic waves

This article proposes a noninvasive liquid level sensing technique using laser-generated ultrasound waves for nuclear power plant applications. Liquid level sensors play an important role of managing the coolant system safely and stably in the plant structure. Current sensing techniques are mostly intrusive, performing inside the fluidic structure, which is disadvantageous in terms of the regular maintenance of the plant system. Furthermore, typical intrusive sensors do not perform stably under varying environmental conditions such as temperature and radiation. In this study, sensing units are attached to the outer surface of a liquid vessel to capture guided ultrasound waves in a nonintrusive manner. The signal intensity of the guided wave dissipates when the signal interacts with the internal liquid media. The sensing mechanism is mathematically expressed as an index value to correlate the liquid level with the sensor signal. For the acoustic wave generation, laser-generated ultrasound was adopted instead of using typical contact type transducers. Following the simulation validation of the proposed concept, the performance of the developed sensor was confirmed through experimental results under elevated liquid temperature conditions. The nonlinear multivariable regression exhibited the best-fit to the datasets measured under the variable liquid level and temperature conditions.

A Review of Laser Ultrasonic Lamb Wave Damage Detection Methods for Thin-Walled Structures

Thin-walled structures, like aircraft skins and ship shells, are often several meters in size but only a few millimeters thick. By utilizing the laser ultrasonic Lamb wave detection method (LU-LDM), signals can be detected over long distances without physical contact. Additionally, this technology offers excellent flexibility in designing the measurement point distribution. The characteristics of LU-LDM are first analyzed in this review, specifically in terms of laser ultrasound and hardware configuration. Next, the methods are categorized based on three criteria: the quantity of collected wavefield data, the spectral domain, and the distribution of measurement points. The advantages and disadvantages of multiple methods are compared, and the suitable conditions for each method are summarized. Thirdly, we summarize four combined methods that balance detection efficiency and accuracy. Finally, several future development trends are suggested, and the current gaps and shortcomings in LU-LDM are highlighted. This review builds a comprehensive framework for LU-LDM for the first time, which is expected to serve as a technical reference for applying this technology in large, thin-walled structures.

Guided wave propagation along surface of vertical solid partially submerged in horizontal liquid layer

Guided waves can propagate along the surface of a solid structure at a large distance with little attenuation. Hidden defects within the structure can be detected based on the abnormal reflection or transmission of guided waves. The distance between the defect and the transducer can be calculated according to the arrival time of the wave package and wave speed. Therefore, the guided wave speed should be known prior. However, existing analytical models can only predict the guided wave speed along the surface of a horizontal solid covered by a liquid layer. These models are not suited for the characterization of ultrasonic nondestructive testing of ship hulls, sluice gates, water dams, bridge piers, etc. In this study, an analytical model is proposed for the guided wave propagation along a vertical solid partially inserted into a horizontal liquid layer. A secular equation is derived and solved to predict the guided wave speed at the vertical solid-liquid interface. Twenty finite element simulations and thirty-three physical experiments are conducted on different materials with different dimensions by using varying incident frequencies. The results demonstrate that the proposed model can provide accurate analytical wave speeds to guide the nondestructive testing and structural health monitoring of underwater structures.

A Method of Damage Detection Efficiency Enhancement of PZT Sensor Networks under Influence of Environmental and Operational Conditions

Two performance parameters are particularly important for the assessment of structural health monitoring (SHM) systems, i.e., their damage detection capabilities and risk of false positive indications due to varying environmental and operational conditions (EOCs). A reduced ratio of false-positive indications can be of significant importance for particular applications, for example, in aerospace, where the costs of unplanned maintenance procedures can be very high. In such cases, the reduction of the false calls ratio can be critical for the possibility of the practical application of the system, apart from damage detection efficiency and system costs. Among various sensor technologies, PZT networks are proven to be one of the most universal approaches to SHM, and they were successfully applied in different scenarios. Moreover, many EOCs which may have an impact on the risk of false positive indications have been identified. Over the years, different approaches to the influence of EOCs compensation have been proposed. Compensation methods can be tailored to the particular way in which a given measurement condition, for example, ambient temperature, alters signals acquired by the PZT network or can be formulated to be also applied in the more general case. In the paper, a method for enhancement of damage detection efficiency under influence of EOCs of general nature is proposed. The particular measurement condition affecting signals acquired by PZT sensors neither needs to be measured, which could be hard in some cases, but also nor even have to be identified. The efficiency of the proposed compensation algorithms is verified based on the example of experimental results obtained under varying temperatures.

Experiments and modelling of ultrasonic waves in composite plates under varying temperature

Guided wave (GW) structural health monitoring (SHM) systems offer an attractive solution as an in-situ quasi real-time assessment of structural damage, but their sensitivity and efficiency may be impaired under varied environmental and operational conditions. Thus, virtual tests, such as that based on the Finite Element (FE) method, represent a valid approach for simulating and investigating SHM systems, enabling a substantial reduction in experimental campaigns. In this work, GW propagation characteristics in a carbon fibre-reinforced composite plate are studied under a varying temperature condition, representative of the aeronautics application. At first, GW SHM system was physically tested at room temperature (20^°C), and the results were used to calibrate and assess the proposed FE modelling approaches, characterised by different element types and mesh sizes. A temperature independent averaged time compensation factor is proposed to mitigate the numerical data dependency on excitation frequency and propagation angle. Two temperature variations (from 20^°C to -50^°C, and 20^°C to 65^°C) were experimentally and numerically considered to investigate the effect of varying temperature on the GW. For all test cases, the compensated numerical data was compared to the experimental results, and discussed in terms of dispersion curves, focusing on the zero-order symmetric, S₀, and antisymmetric, A₀, modes. Results show that both 2D and 3D FE approaches can accurately predict the changes in GW due to varying temperature, with the group velocity of the A₀ mode being less sensitive to temperature variations.

Mechanic-Electric-Thermal Directly Coupling Simulation Method of Lamb Wave under Temperature Effect

Lamb Wave (LW)-based structural health monitoring method is promising, but its main obstacle is damage assessment in varying environments. LW simulation based on piezoelectric transducers (referred to as PZTs) is an efficient and low-cost method. This paper proposes a multiphysics simulation method of LW propagation with the PZTs under temperature effect. The effect of temperature on LW propagation is considered from two aspects. On the one hand, temperature affects the material parameters of the structure, the adhesive layers and the PZTs. On the other hand, it is considered that the thermal stress caused by the inconsistency of thermal expansion coefficients among the structure, the adhesive layers, and the PZTs affect the piezoelectric constant of the PZTs. Based on the COMSOL Multiphysics, the mechanic-electric-thermal directly coupling simulation model under temperature effect is established. The simulation model consists of two steps. In the first step, the thermal-mechanic coupling is carried out to calculate the thermal stress, and the thermal stress effect is introduced into the piezoelectric constant model. In the second step, mechanic-electric coupling is carried out to simulate LW propagation, which considers the piezoelectric effect of the PZTs for the LW excitation and reception. The simulation results at -20 °C to 60 °C are obtained and compared to the experiment. The results show that the A₀ and S₀ mode of simulation signals match well with the experimental measurements. Additionally, the effect of temperature on LW propagation is consistent between simulation and experiment; that is, the amplitude increases, and the phase velocity decreases with the increment of temperature.

Sliding Window Dynamic Time-Series Warping-Based Ultrasonic Guided Wave Temperature Compensation and Defect Monitoring Method for Turnout Rail Foot

Temperature changes are a major challenge in outdoor guided wave structural health monitoring of rails. Temperature variations greatly impact the waveform of guided wave signals, making it challenging to diagnose and characterize defects. Traditional temperature compensation methods, such as signal stretch and scale transform, are restricted to use in regular structures, such as plates and pipes. To solve the temperature compensation problem in long rails with serious mode conversion and complex structure echo, we propose a temperature compensation and defect monitoring method, namely, sliding window dynamic time-series warping (SWDTW), which overcomes the challenges of mass computation and overcompensation of dynamic time-series warping (DTW). The basic idea of SWDTW is to utilize sliding windows to accelerate the computation and identify defects from subsequence scales. Then, an index, window subsequence Teager energy (WSTE), is used to indicate the local abnormality of guided wave signals, and a sliding window net (SWnet) is devised to monitor the occurrence of defects automatically. Outdoor monitoring of turnout rails showed that the proposed method can effectively reduce the temperature noise and recognize an artificial defect with 1.16% and 0.36% cross-sectional change rates (CSCRs) on the switch and stock rails, respectively, at different temperatures; moreover, the defect signals processed by SWDTW showed better defect identification performance than those processed by scale transform and DTW.

Lamb Wave Detection for Structural Health Monitoring Using a ϕ-OTDR System

In this paper, the use of a phase-sensitive optical time-domain reflectometry (ϕ-OTDR) sensor for the detection of the Lamb waves excited by a piezoelectric transducer in an aluminum plate, is investigated. The system is shown to detect and resolve the Lamb wave in distinct regions of the plate, opening the possibility of realizing structural health monitoring (SHM) and damage detection using a single optical fiber attached to the structure. The system also reveals the variations in the Lamb wave resulting from a change in the load conditions of the plate. The same optical fiber used to detect the Lamb waves has also been employed to realize distributed strain measurements using a Brillouin scattering system. The method can be potentially used to replace conventional SHM sensors such as strain gauges and PZT transducers, with the advantage of offering several sensing points using a single fiber.

Damage identification using wave damage interaction coefficients predicted by deep neural networks

The ever-increasing demand for efficiency and cost improvements in lightweight structures with guaranteed safety and reliability is leading to the application of a damage-tolerant design philosophy. Here, accurate knowledge of structural health is critical to avoid catastrophic failures. This knowledge can be obtained by using advanced structural health monitoring (SHM) systems. For thin-walled lightweight structures, methods utilizing guided waves generated by piezoelectric transducers are well suited. The interaction between the guided waves and potential damages can be described by so-called wave damage interaction coefficients (WDICs). These WDICs are unique for each damage and depend solely on its characteristics for a given structure. Therefore, the comparison of known WDICs with estimated ones allows drawing conclusions about the current structural state. In this paper, a novel damage identification method for plate-like structures based on a database of such WDICs is presented. Selected damages are simulated numerically with finite elements to generate WDIC patterns. However, these simulations are computationally highly demanding, thus only a very limited number of damage scenarios can be simulated. This study proposes an innovative technique to substantially enhance the resulting WDIC database by using deep neural networks (DNNs). These DNNs enable smart interpolations and allow not only predicting WDICs for previously unseen damages at low computational costs but also the discovery of knowledge about the complex relationship between damage features and WDIC patterns. A comparison to other machine learning algorithms clearly shows the superior performance of the utilized DNNs for interpolating complex WDIC patterns. The proposed damage identification method is verified using advanced time-domain simulations of a large aluminum plate. A statistical analysis of correct identification rates in a common three-sensor setting is employed for assessing the general performance. It is demonstrated that carefully identified DNNs enable to accurately replicate and interpolate complex WDIC patterns. Furthermore, it is shown that these predicted WDICs allow identifying damage characteristics with high confidence.

Finite Element Modeling Approaches, Experimentally Assessed, for the Simulation of Guided Wave Propagation in Composites

Today, structural health monitoring (SHM) systems based on guided wave (GW) propagation represent an effective methodology for understating the structural integrity of primary and secondary structures, also made of composite materials. However, the sensitivity to damage detection promoted by these systems can be altered by such factors as the geometry of the monitored parts, as well as the environmental and operational conditions (EOCs). Experimental investigations are fundamental but require a long time period and are costly, especially for tests in real-life scenarios. Experimentally validated simulations can help designers to improve SHM effectiveness due to the possibility of further broadening study on the different geometries, load cases, and material types with less effort. From this point of view, this paper presents two finite element (FE) modeling approaches for the simulation of GW propagation in composite panels. The case study consists of a flat and a curved composite panel. The two approaches herein investigated are based on implicit and explicit finite element analysis (FEA) formulations. The comparison of the predicted measures against the experimental dataset allowed the assessment of the levels of accuracy provided by both modeling approaches with respect to the dispersion curves. Furthermore, to assess the different curvature sensitivities of the proposed numerical and experimental approaches, the extracted dispersion curves for both flat and curved panels were compared.

The Need for Multi-Sensor Data Fusion in Structural Health Monitoring of Composite Aircraft Structures

With the increased use of composites in aircraft, many new successful contributions to the advancement of the structural health monitoring (SHM) field for composite aerospace structures have been achieved. Yet its application is still not often seen in operational conditions in the aircraft industry, mostly due to a gap between research focus and application, which constraints the shift towards improved aircraft maintenance strategies such as condition-based maintenance (CBM). In this work, we identify and highlight two key facets involved in the maturing of the SHM field for composite aircraft structures: (1) the aircraft maintenance engineer who requires a holistic damage assessment for the aircraft’s structural health management, and (2) the upscaling of the SHM application to realistic composite aircraft structures under in-service conditions. Multi-sensor data fusion concepts can aid in addressing these aspects and we formulate its benefits, opportunities, and challenges. Additionally, for demonstration purposes, we show a conceptual design study for a fusion-based SHM system for multi-level damage monitoring of a representative composite aircraft wing structure. In this manner, we present how multi-sensor data fusion concepts can be of benefit to the community in advancing the field of SHM for composite aircraft structures towards an operational CBM application in the aircraft industry.

Imaging damage in plate waveguides using frequency-domain multiple signal classification (F-MUSIC)

Earlier, an ameliorated MUSIC (Am-MUSIC) algorithm is developed by the authors [1], aimed at expanding conventional MUSIC algorithm from linear array-facilitated nondestructive evaluation to in situ health monitoring with a sparse sensor network. Yet, Am-MUSIC leaves a twofold issue to be improved: i) the signal representation equation is constructed at each pixel across the inspection region, incurring high computational cost; and ii) the algorithm is applicable to monochromatic excitation only, ignoring signal features scattered out of the excitation frequency band which also carry information on structural integrity. With this motivation, a multiple-damage-scattered wavefield model is developed, with which the signal representation equation is constructed in the frequency domain, avoiding computationally expensive pixel-based calculation - referred to as frequency-domain MUSIC (F-MUSIC). F-MUSIC quantifies the orthogonal attributes between the signal subspace and noise subspace inherent in signal representation equation, and generates a full spatial spectrum of the inspected sample to visualize damage. Modeling in the frequency domain endows F-MUSIC with the capacity to fuse rich information scattered in a broad band and therefore enhance imaging precision. Both simulation and experiment are performed to validate F-MUSIC when used for imaging single and multiple sites of damage in an isotropic plate waveguide with a sparse sensor network. Results accentuate that effectiveness of F-MUSIC is not limited by the quantity of damage, and imaging precision is not downgraded due to the use of a highly sparse sensor network - a challenging task for conventional MUSIC algorithm to fulfil.

Structural Health Monitoring in Composite Structures: A Comprehensive Review

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

Optical Phenomena in Mesoscale Dielectric Particles

During the last decade, new unusual physical phenomena have been discovered in studying the optics of dielectric mesoscale particles of an arbitrary three-dimensional shape with the Mie size parameter near 10 (q~10). The paper provides a brief overview of these phenomena from optics to terahertz, plasmonic and acoustic ranges. The different particle configurations (isolated, regular or Janus) are discussed, and the possible applications of such mesoscale structures are briefly reviewed herein in relation to the field enhancement, nanoparticle manipulation and super-resolution imaging. The number of interesting applications indicates the appearance of a new promising scientific direction in optics, terahertz and acoustic ranges, and plasmonics. This paper presents the authors’ approach to these problems.

Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method

Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage identification and localization problem, according to a Structural Health Monitoring approach. Finite Element (FE) Analysis can be extremely helpful, especially for reducing the laborious experimental campaign costs for the ANN development and training phases. The aim of the present work is to propose a guided wave-based ANN, developed through the use of the Finite Element Method, to determine the position of damages. The paper first addresses the development and assessment of the modeling technique. The FE model accuracy was proven through the comparison of the predicted results with experimental and analytical data. Then, the ANN was developed and trained on an aluminum plate and subsequently verified in a composite plate, as well as under different damage configurations. According to the results herein proposed, the ANN allowed to detect and localize damages with a high level of accuracy in all cases of study.

RETRACTED: Guided ultrasonic wave-based investigation on the transient response in an axisymmetric viscoelastic cylindrical waveguide

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief, for the unauthorized use of third party research data by the author. The author apologizes for the violation of the journal's code of ethics. One of the conditions of submission of a paper for publication in Ultrasonics is that authors must declare explicitly that their work is original. Use of research outcomes from others without formal permission or without appropriate citation is regarded as an ethical violation. The scientific community takes a very strong view on this matter and apologies are offered to readers of Ultrasonics that this was not detected during the submission process.

Lamb Wave Based Structural Damage Detection Using Stationarity Tests

Lamb waves have been widely used for structural damage detection. However, practical applications of this technique are still limited. One of the main reasons is due to the complexity of Lamb wave propagation modes. Therefore, instead of directly analysing and interpreting Lamb wave propagation modes for information about health conditions of the structure, this study has proposed another approach that is based on statistical analyses of the stationarity of Lamb waves. The method is validated by using Lamb wave data from intact and damaged aluminium plates exposed to temperature variations. Four popular unit root testing methods, including Augmented Dickey-Fuller (ADF) test, Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, Phillips-Perron (PP) test, and Leybourne-McCabe (LM) test, have been investigated and compared in order to understand and make statistical inference about the stationarity of Lamb wave data before and after hole damages are introduced to the aluminium plate. The separation between t-statistic features, obtained from the unit root tests on Lamb wave data, is used for damage detection. The results show that both ADF test and KPSS test can detect damage, while both PP and LM tests were not significant for identifying damage. Moreover, the ADF test was more stable with respect to temperature changes than the KPSS test. However, the KPSS test can detect damage better than the ADF test. Moreover, both KPSS and ADF tests can consistently detect damages in conditions where temperatures vary below 60 °C. However, their t-statistics fluctuate more (or less homogeneous) for temperatures higher than 65 °C. This suggests that both ADF and KPSS tests should be used together for Lamb wave based structural damage detection. The proposed stationarity-based approach is motivated by its simplicity and efficiency. Since the method is based on the concept of stationarity of a time series, it can find applications not only in Lamb wave based SHM but also in condition monitoring and fault diagnosis of industrial systems.

Environmental Effects on Piezoelectric Sensors Array Signals and a Compensated Damage Imaging Method

Piezoelectric sensors array based damage imaging method as a high resolution source localization algorithm is becoming a promising method in structural health monitoring (SHM) technology. However, the environmental variations could affect the gain-phase of array signal. This paper experimentally evaluates the environmental effects on piezoelectric sensors array, and presents a compensated 2D-MUSIC based damage imaging method for composite structures. Firstly, detailed analysis and comparison discussion about the gain-phase difference of array signal when the environmental parameters change, and the gain-phase changes respect to the environmental parameters could be obtained. Secondly, array error matrix is structured and substituted into the steering vector of the original 2D-MUSIC algorithm to compensate. Finally, the compensated 2D-MUSIC algorithm is applied for estimating the initial estimates of damage. After substituting these initial estimates, the cost function is minimized by adaptive iterative calculating the reasonable location of the damage source. The experiments on an epoxy laminate plate demonstrate the validity and effectiveness of the proposed method.

Broadband ultra-long acoustic jet based on double-foci Luneburg lens

In this paper, a gradient index acoustic metamaterial is proposed based on the concept of the optical modified generalized Luneburg lens (MGLL). With the MGLL, double-foci and high energy density between the two foci can be achieved, which enables the realization of an ultra-long acoustic jet between the two foci. This capability of the MGLL is theoretically and numerically demonstrated with an acoustic metamaterial lens. Numerical simulation results show that based on this design, ultra-long acoustic jets with a jet length of up to 30 λ can be achieved, covering both the near field and far field.

Modeling lamb wave propagation in visco-elastic composite plates using a fifth-order plate theory

A new extension of the shear deformation theory to fifth order in order to calculate the spectrum of Lamb waves in orthotropic media over a wide frequency range is developed and analyzed. The aspiration of the proposed method is to create an alternative framework to exhaustive 3D elasticity based solutions by increasing computational efficiency without losing accuracy, nor robustness. A new computational framework is introduced which allows to estimate the dispersion curves for the first nine symmetric and nine anti-symmetric Lamb modes. Analytically calculated dispersion curves using 5-SDT for different propagation directions and polar plots for selected frequency of different materials are compared with the results from both the semi analytical finite element method, and lower order shear deformation theories. Careful analysis for individual laminae and for symmetric composite laminates exhibits a good agreement between the new higher order plate theory and the semi analytical finite element method over an extensive frequency range. In addition, attenuation plots show that the proposed method can also be used for visco-elastic materials (or highly damped materials). The advantage of the new higher order plate theory and its numerical implementation is that it is much more computationally efficient compared to comprehensive methods as Lamb wave polar plots of composite plates as function of incidence angle, polar angle and frequency can be calculated in less than a second on a standard laptop. Consequently, the use of this framework in inversion routines opens up the possibility of quasi real-time Structural Health Monitoring for visco-elastic composites covering a sufficiently wide frequency range.

Cure monitoring and damage identification of CFRP using embedded piezoelectric sensors network

Because of the advantages of high specific strength and high specific stiffness, carbon fiber reinforced plastics (CFRP) have been the most ideal materials in the field of civil aviation. Cure monitoring in manufacturing process and damage identification in service stage of CFRP are always hot topics. The Semi-Analytical Finite Element (SAFE) method and micromechanical model are employed to analyse the propagation characteristics of the Lamb-like waves in a continuous flat aluminium plate attached to a viscoelastic unidirectional CFRP in semi-infinite half-space. Then the vacuum bag moulding process of prepregs is monitored using Fiber Bragg Grating (FBG) and piezoelectric sensors encapsulated in Stanford Multiactuator-Receiver Transduction (SMART) Layer. The calculated energy velocities and attenuations of guided waves show the same trends with the numerical results while curing. After the CFRP is demoulded, the damage identification experiments are carried out. By continuing to use the sensor network embedded in the manufacturing phase, the artificial damages can be precisely located. The results demonstrated that the life-cycle monitoring of CFRP can be achieved effectively by the piezoelectric sensors network.

Structural Damage Identification Using a Modified Directional Bat Algorithm

Bat algorithm (BA) has been widely used to solve optimization problems in different fields. However, there are still some shortcomings of standard BA, such as premature convergence and lack of diversity. To solve this problem, a modified directional bat algorithm (MDBA) is proposed in this paper. Based on the directional bat algorithm (DBA), the individual optimal updating mechanism is employed to update a bat’s position by using its own optimal solution. Then, an elimination strategy is introduced to increase the diversity of the population, in which individuals with poor fitness values are eliminated, and new individuals are randomly generated. The proposed algorithm is applied to the structural damage identification and to an objective function composed of the actual modal information and the calculated modal information. Finally, the proposed MDBA is used to solve the damage detection of a beam-type bridge and a truss-type bridge, and the results are compared with those of other swarm intelligence algorithms and other variants of BA. The results show that in the case of the same small population number and few iterations, MDBA has more accurate identification and better convergence than other algorithms. Moreover, the study on anti-noise performance of the MDBA shows that the maximum relative error is only 5.64% at 5% noise level in the beam-type bridge, and 6.53% at 3% noise in the truss-type bridge, which shows good robustness.

Ultrasonic wireless power links for battery-free condition monitoring in metallic enclosures

This paper presented a novel ultrasonic wireless power link (UWPL) to provide power supply for embedded condition monitoring of enclosed metallic structures, where recharging or replacing batteries can be problematic. Two piezoelectric transducers are adopted to establish the wireless power links, within which one transducer is used to generate ultrasonic waves and the other is to receive the transferred ultrasonic energy and to energize the associated embedded condition monitoring units. A power management solution is established to regulate the receiver output into a constant voltage suitable for sensing application. A theoretical model was established to understand the UWPL dynamics and to analyze the energy budget balance between the UWPL and the sensing power demands. A finite element model was built to validate the proposed idea. The UWPL was then experimentally implemented using two piezoelectric transducers and tested in aluminium plates with different thickness. A power management sub-system was developed and tested for sensing applications. An output power of 1.73 mW was obtained on a 1.5 kΩ resister with the input voltage of 15 V at 42.6 kHz through a 6 mm-thick aluminium plate. Sufficient power can be transferred over a large distance via metallic structures, showing the capability in implementing battery-free condition monitoring of enclosed metallic structures, such as petroleum pipelines, engines, and aluminium airframe.

Shear horizontal wave transducers for structural health monitoring and nondestructive testing: A review

Shear horizontal (SH) waves are of great importance in structural health monitoring (SHM) and nondestructive testing (NDT), since the lowest order SH wave in isotropic plates is non-dispersive. The SH waves in plates, circumferential SH waves and torsional waves in pipes have remarkable resemblances in dispersion characteristics and wave structures, so the latter two can also be called as SH waves in pipes. This paper reviews the state-of-the-art research on SH wave transducers for SHM and NDT. These transducers are grouped into the following categories: Lorentz-force-based electromagnetic acoustic transducers (EMATs), magnetostrictive EMATs, shear wave piezoelectric wedge transducers, thickness-shear piezoelectric transducers and face-shear piezoelectric transducers. The working principles, applications, merits and limitations of different kinds of SH wave transducers are summarized, with a focus on discussing the various configurations for exciting and receiving directional, omnidirectional SH waves in plates and torsional waves in pipes. This paper is expected to greatly promote the applications of SH waves in SHM, NDT and the related areas such as elastic metamaterials.

Quantitative defect inspection in the curved composite structure using the modified probabilistic tomography algorithm and fusion of damage index

The curved composite structures are popularly used in the aerospace field for their superior properties. Complexity of structure and geometry generally limit the inspection or monitoring effect of different types of defects in the curved composite structure. A feasible damage probabilistic tomography algorithm combined with ultrasonic guided wave technology is necessary to be developed for the structural health monitoring of curved composite structures. In this paper, defect zones in a curved composite structure are characterized using the modified probabilistic tomography (MPT) method and fusion of damage index (DI). The MPT with the defect shape factor β_M at each damage-impaired path and hybrid DI schemes are proposed to indicate the location and propagation of defect zones in tested sample. The feasibility of proposed approaches is verified on the curved carbon/epoxy composite structure, experimentally. The results show that the MPT and fusion DI methods successfully represent the extension of defect zones in a quantitative manner. It is suggested that the accuracy and reliability of localization results of the MPT algorithm is better than those obtained by the probabilistic tomography (PT) algorithm with the averaged β.

Piezoelectricity in Structural Adhesives and Application for Monitoring Joint Integrity via Guided Ultrasonic Waves

In an adhesively bonded structure, utilizing the adhesive itself for monitoring the joint integrity can be beneficial in reduction of labor, time, and potential human errors while avoiding problems associated with introduction of a foreign sensor component. This work started from the examination of effective piezoelectricity of commercial structural adhesives/sealants, and five of them were found to possess effective piezoelectric property, with effective piezoelectric coefficient d₃₃ from -0.11 to -1.77 pm/V depending on frequency under substrate clamping condition. With stable piezoelectric response at least up to megahertz, an epoxy adhesive with inorganic filler was selected for structural health monitoring (SHM) feasibility demonstration via generating or sensing guided ultrasonic Lamb waves. The presence of disbond in the adhesive joint is detectable by comparing the Lamb waves signal with a reference baseline signal associated with an intact structure. The results show that the selected adhesive with piezoelectric response can perform the dual roles of structural bonding and ultrasonic joint integrity monitoring.

Lamb wave-based mapping of plate structures via frontier exploration

Substantial improvements in material processing and manufacturing techniques in recent years necessitate the introduction of effective and efficient nondestructive testing (NDT) methods that can seamlessly integrate into day-to-day aircraft and aerospace operations. Lamb wave-based methods have been identified as one of the most promising candidates for the inspection of large-scale structures. At the same time, there is presently a high level of research in the field of autonomous mobile robotics, especially in simultaneous localization and mapping (SLAM). Thus, this paper investigates a means to automate Lamb wave-based NDT by positioning sensors along a planar structure through mobile service robots. To this end, a generalized method for the mapping of plate structures using scattered Lamb waves by means of frontier exploration is presented such that an autonomous SLAM-capable NDT system can become realizable. The performance of this novel Lamb wave-based frontier exploration is first evaluated in simulation. It is shown that it generally outperforms a random frontier exploration and may even perform near-optimal in the case of an isotropic, square panel. These findings are then validated in laboratory experiments, confirming the general feasibility of utilizing Lamb waves for SLAM. Furthermore, the versatility of the developed methodology is successfully demonstrated on a more complexly shaped stiffened panel.