Ultrasonic wavefield imaging: Research tool or emerging NDE method?

Michaels, Jennifer E.

doi:10.1063/1.4974542

Cited by 26 publications

(20 citation statements)

References 37 publications

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“…or equivalently The optimal shift t is determined by solving this least-squares optimization in the frequency domain. We solve the problem in the frequency domain since t in equation 9can be any real number and is not restricted to an integer sample as in equation (8). This optimization problem is not generally convex (i.e.…”

Section: Methodology: Isolating Damage Signaturesmentioning

confidence: 99%

“…Yet while full wavefield analysis provides significant benefits, it has two significant challenges: slow data acquisition as well as a need for statistical reliability and better data analysis. 8 Wavefields are often generated with a piezoelectric transducer and then measured at scanning locations with a laser Doppler vibrometer (LDV). 1 Alternatively, wavefields can be generated at scanning locations with a pulse laser and then measured from a pizeoelectric transducer.…”

Section: Introductionmentioning

confidence: 99%

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Statistical partial wavefield imaging using Lamb wave signals

Harley

Chia

2017

Structural Health Monitoring

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This article presents a baseline-free, model-driven, statistical damage detection and imaging framework for guided waves measured from partial (i.e. non-dense) wavefield scans. Wavefield analysis is an effective non-contact technique for nondestructive evaluation. Yet, there are several limitations to practically implement wavefield methods. These limitations include slow data acquisition and a lack of statistical reliability. Our approach addresses both of these challenges. We use sparse wavenumber analysis, sparse wavenumber synthesis, and data-fitting optimization to accurately model damagefree wavefield data. We then combine this model with matched field processing to image damage from a small number of partial wavefield measurements. We further derive a hypothesis test based on extreme value theory to statistically detect damage. We test our framework with Lamb wave measurements from a steel plate. With 70 experimental wavefield measurements, we achieve an empirical probability of damage detection of more than 98%, an empirical probability of false alarm of less than 0.17%, and an accurate image of the damage.

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Section: Methodology: Isolating Damage Signaturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical partial wavefield imaging using Lamb wave signals

Harley

Chia

2017

Structural Health Monitoring

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“…Ultrasonic imaging alludes to securing full waveform data over the interrogated region for waves generated by stationary source [31] and becomes an emerging tool for damage identification. Several guided wave-based imaging studies have noted various types of defects such as cracks [32], corrosion [33], and impact damage [34] and applying conventional techniques for damage identification such as delay and sum imaging [35], minimum variance imaging [36], and the combination of both [37].…”

Section: A0-mode Lamb Wave Based Detection Of Corrosion Under Coatingmentioning

confidence: 99%

A0-Mode Lamb Wave Based Detection of Corrosion under Coating Film in Plate-Like Metallic Structures: Analytical, Finite Element and Experimental Studies

Rabbi¹,

Teramoto²

2018

GUB JOURNAL OF SCIENCE AND ENGINEERING

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The fundamental order antisymmetric mode (A0-mode) Lamb wave based a quantitative acoustical imaging technique is proposed in this paper to classify the corroded region under coating film within the metallic structure. This method estimates versatility qualities of the zone of intrigue to some degree inhomogeneity differentiated to the encompassed materials. However, a converging region consisting of incident wave field and scattered wave field difficult to demonstrate from the observed signals. The proposed method focuses on reconstructing the image of the defect by calculating the shear strains. The shear strains deduced from the observed normal displacement of the specimen. A covariance matrix has been constructed using the shear strains and meaningful determinant values of the matrix reveal the overlapping region. As the beginning overlapping region developed at the edge of the defect, thus image of the defect can be reconstructed by this technique. This paper discussed the analytical approach of computing the incoming, outgoing, as well as the transmitting wave fields. Physical interpretation of the analytical prediction is explored via numerical simulations and acoustical observations. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 5(1), Dec 2018 P 37-44

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“…In addition to accelerating the scanning time, which is one of the disadvantages of UWPI, researchers [ 41 ] have presented a novel approach to solve this problem by using compressed sensing and DL algorithms. With these advantages, UWPI has become a promising tool in both NDE and structural health monitoring (SHM) [ 42 ]. Some studies have already applied wavefield imaging for detecting loose bolts [ 43 , 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Bolt Loosening Diagnosis Using Deep Learning Algorithms for Laser Ultrasonic Wave Propagation Data

Tran

Kim

Tola

et al. 2020

Sensors

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The application of deep learning (DL) algorithms to non-destructive evaluation (NDE) is now becoming one of the most attractive topics in this field. As a contribution to such research, this study aims to investigate the application of DL algorithms for detecting and estimating the looseness in bolted joints using a laser ultrasonic technique. This research was conducted based on a hypothesis regarding the relationship between the true contact area of the bolt head-plate and the guided wave energy lost while the ultrasonic waves pass through it. First, a Q-switched Nd:YAG pulsed laser and an acoustic emission sensor were used as exciting and sensing ultrasonic signals, respectively. Then, a 3D full-field ultrasonic data set was created using an ultrasonic wave propagation imaging (UWPI) process, after which several signal processing techniques were applied to generate the processed data. By using a deep convolutional neural network (DCNN) with a VGG-like architecture based regression model, the estimated error was calculated to compare the performance of a DCNN on different processed data set. The proposed approach was also compared with a K-nearest neighbor, support vector regression, and deep artificial neural network for regression to demonstrate its robustness. Consequently, it was found that the proposed approach shows potential for the incorporation of laser-generated ultrasound and DL algorithms. In addition, the signal processing technique has been shown to have an important impact on the DL performance for automatic looseness estimation.

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Ultrasonic wavefield imaging: Research tool or emerging NDE method?

Cited by 26 publications

References 37 publications

Statistical partial wavefield imaging using Lamb wave signals

Statistical partial wavefield imaging using Lamb wave signals

A0-Mode Lamb Wave Based Detection of Corrosion under Coating Film in Plate-Like Metallic Structures: Analytical, Finite Element and Experimental Studies

Artificial Intelligence-Based Bolt Loosening Diagnosis Using Deep Learning Algorithms for Laser Ultrasonic Wave Propagation Data

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