Multi-mode reverse time migration damage imaging using ultrasonic guided waves

He, Jiaze; Leckey, Cara A. C.; Leser, Patrick E.; Leser, William P.

doi:10.1016/j.ultras.2018.08.005

Cited by 46 publications

(24 citation statements)

References 46 publications

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“…As detailed in He and Yuan 53 and He et al, 71 the conventional RTM contains three steps: the simulation of the forward wavefield, the back-propagation of the time-reversed received signals, and the application of imaging conditions. With proper imaging conditions, 54 the damage reflectivity can be obtained in the images.…”

Section: Theoretical Formulation Of Lsrtmmentioning

confidence: 99%

Guided wave tomography based on least-squares reverse-time migration

Rocha

Sava

2019

Structural Health Monitoring

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A key to successful damage diagnostics and quantification is damage imaging through ultrasonic guided wave tomography. We propose the implementation of least-squares reverse-time migration in a circular array for damage imaging in an aluminum plate. The theory of least-squares reverse-time migration is formulated for guided wave applications along with the summary of an efficient optimization algorithm: the conjugate gradient method. Numerical simulation and laboratory experiments are used to evaluate its performance with a circular array setup. In order to improve the data processing efficiency, the concept of using a limited number of actuators but a relatively large number of sensors is tested. Studies are conducted on three numerical cases, including a rectangular-shaped damage site, a complex-shaped damage site, and six other damage sites varying in size. As an inversion-based method, least-squares reverse-time migration shows significantly improved shape reconstruction with the amplitude quantification capability, compared to conventional reverse-time migration. Our experimental data are generated by piezoelectric wafers as actuators, measured by a scanning laser Doppler vibrometer to form a circular array on an aluminum plate, with a rectangular notch located in the inner region of the array. The damage images using experimental data show consistency in both the simulations using Born scattering and in altered material properties in the damaged region. According to the comparison, least-squares reverse-time migration for guided wave tomography is a promising technology to provide high-resolution large area damage imaging for plate-like structures.

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Section: Theoretical Formulation Of Lsrtmmentioning

confidence: 99%

Guided wave tomography based on least-squares reverse-time migration

Rocha

Sava

2019

Structural Health Monitoring

Self Cite

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“…Several types of transducers for guided wave actuation and sensing have also been employed such as ultrasonic probes, 46 piezoelectric wafer active sensors, 47,48 laser-based ultrasonics, 49 and fibreoptic sensors 50 for the health monitoring of isotropic and anisotropic structures. Based on these measurements, various algorithms were developed for damage identification, including time of flight, 51 time reversal, 52,53 migration technique, 54 tomography, 55 and phased-array beamforming. 56 In this study, Lamb wave technique based on piezoelectric active wafer sensors [57][58][59] is adopted to validate the numerical results by calculating the wave velocity.…”

Section: Introductionmentioning

confidence: 99%

A homogenisation scheme for ultrasonic Lamb wave dispersion in textile composites through multiscale wave and finite element modelling

Thierry

Cantero‐Chinchilla

et al. 2021

Struct Control Health Monit

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Summary In this work, homogenisation of textile composite models is studied in order to increase the accuracy of wave dispersion predictions in such complex structures. A multiscale methodology is formed involving (i) calculation of the ultrasonic wave propagation through a detailed mesoscale finite element model of the textile and (ii) updating the mechanical properties of a semi‐analytical finite element (SAFE) model to match the accurate mesoscale predictions. The speeds of the first shear (SH0) and Lamb wave modes (A0 and S0) are used to define the objective function to minimise in an inversion process based on genetic algorithms. The algorithm is tested on an orthotropic plate structure whose all nine unknown elastic moduli are successfully reconstructed and is then applied to three different numerical models. We demonstrate both numerically and experimentally that the proposed scheme provides more accurate dispersion characteristics compared to the ones obtained through the statically measured mechanical properties.

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“…4–7 Interrogating large areas with a small number of sensors is the common system configuration using UGW, and then damage location and severity, based on the propagation distances 8 and guided wave (GW) amplitude, 9 can be obtained from the received signals. Numerous UGW-based damage localization methods have been developed in recent years, such as the triangulation method, 10 focusing array algorithm, 11 GW phased array, 12 migration technique, 13–17 and time-reversal imaging method. 18 All of these methods ultimately estimate the damage locations based on the propagation distance.…”

Section: Introductionmentioning

confidence: 99%

Sparse Bayesian learning approach for propagation distance recognition and damage localization in plate-like structures using guided waves

Zhao

Zhou

Huang

et al. 2020

Structural Health Monitoring

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In ultrasonic guided wave–based damage detection, the propagation distance recognition of wave packets is an essential step. However, it is difficult to perform direct distance extraction from guided wave signals since the multimode, mode conversion, and dispersion effects typically lead to wave packet overlapping and distortion. In addition, the identified damage location may be incorrect due to inevitable uncertainties in the procedure of propagation distance recognition and damage localization. Motivated by these difficulties, a novel two-stage approach for propagation distance recognition and damage localization is proposed based on sparse Bayesian learning framework. In the first stage, prior knowledge of a small number of wave packets contained in a signal is exploited to sparsely represent the guided wave signal and then the corresponding propagation distance and amplitude information of each wave packet can be obtained. In the next stage, only a small number of damages occurring in a structure are exploited and a vector consisting of the propagation distances extracted from the previous stage is used to match the atoms in a pre-defined over-complete distance dictionary matrix, to achieve our goal of localizing structural damage. Both procedures of the two stages are realized by the sparse Bayesian learning algorithm, which obtains the most probable value and the corresponding uncertainty. A sampling strategy is presented to transfer the uncertainty of the propagation distance recognition to the subsequent damage localization. Finally, the effectiveness of the proposed method is validated using numerical simulation and experimental investigation on aluminum plates. The proposed method is only valid for single damage localization in the present form, but it has the potential to be extended for multiple damage localization.

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Multi-mode reverse time migration damage imaging using ultrasonic guided waves

Cited by 46 publications

References 46 publications

Guided wave tomography based on least-squares reverse-time migration

Guided wave tomography based on least-squares reverse-time migration

A homogenisation scheme for ultrasonic Lamb wave dispersion in textile composites through multiscale wave and finite element modelling

Sparse Bayesian learning approach for propagation distance recognition and damage localization in plate-like structures using guided waves

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