Solitary waves for Non-Destructive Testing applications: Delayed nonlinear time reversal signal processing optimization

Lints, Martin; Santos, Serge Dos; Salupere, Andrus

doi:10.1016/j.wavemoti.2016.07.001

Cited by 21 publications

(18 citation statements)

References 21 publications

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“…Note that when using the modal approach the data are obtained synthetically using the Born approximation as can be seen in Eq. (19) while in imaging with I a (x), the data are obtained by solving the wave equation. Finally, Fig.…”

Section: Defect Localization In 1dmentioning

confidence: 99%

“…Ambient noise measurements may be used as the primary recordings that are being time reversed, as in [16], but this is a slightly different problem than the one considered in this paper. Other types of difficulties that have been successfully treated by TR, include but are not limited to, media with random properties [17], multiple scattering in granular media [18], non-linear problems [19], multiple scatterer localization [20] and damping [21,22,23]. Consideration of damping is a critical issue in TR because it breaks the non-dissipation condition, necessary for time-reversibility of the wave equation.…”

Section: Introductionmentioning

confidence: 99%

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Signal-to-Noise Ratio analysis for time-reversal based imaging techniques in bounded domains

2018

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We consider the problem of localizing small material defects in rectangular bounded domains. The scalar acoustic equation is used to model wave propagation in this context. Our data is the scattered field collected at one or more receivers and due to impulsive excitations at one or more source positions. To localize the defect we use an imaging method that consists in back-propagating the recorded field in the domain of interest. The back-propagation is performed numerically using a model for the Green's function in the bounded medium. For the source localization problem this imaging technique is equivalent to computational Time Reversal (TR). We study in this paper the quality of imaging in terms of the Signal to Noise Ratio (SNR) both for the source and the defect localization problems. SNR here is defined as the value of the image at the true source (defect) location, divided by the maximal value of the image outside a small region around the true source (defect) location. Our theoretical analysis carried out for the simpler one-dimensional case allows us to correctly predict the performance of the method. Our results indicate that for the source localization problem the SNR increases linearly with the number of receivers while for the defect localization its maximal value is 2 and can only be attained by

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Section: Defect Localization In 1dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signal-to-Noise Ratio analysis for time-reversal based imaging techniques in bounded domains

2018

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“…Nonlinear ultrasound, commonly known in the ultrasonic scientific community as nonlinear elastic wave spectroscopy (NEWS) techniques, is a family of ultrasonic inspection methods that have shown higher sensitivity to the detection and localisation of damage (material flaws and micro-cracks) at early stages of formation than linear ultrasound (Boccardi et al, 2018;Zagrai et al, 2008). NEWS methods can rely on higher harmonic generation (Buck et al, 1978;Ciampa et al, 2017;Fierro and Meo, 2019;Polimeno et al, 2010;Scarselli et al, 2017;Solodov, 2014), time reversal (Ciampa and Meo, 2012;Lints et al, 2017;Liu et al, 2015), wave modulation (Chrysochoidis et al, 2011;Dionysopoulos et al, 2018;Fierro and Meo, 2018;Klepka et al, 2014;Pieczonka et al, 2015;Van Den Abeele et al, 2000b), phase modulation (Vila et al, 2004), local defect resonance (Post et al, 2017;Segers et al, 2018;Solodov et al, 2015), imaging of nonlinear scatters (Haupert et al, 2017;Solodov and Busse, 2012) and shift in resonance frequency characteristics (Chakrapani et al, 2015;Polimeno and Meo, 2009;Solodov, 2018;Van Den Abeele et al, 2000a).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear ultrasonic inspection of smart carbon fibre reinforced plastic composites with embedded piezoelectric lead zirconate titanate transducers for space applications

Andreades

Fierro

Meo

et al. 2019

Journal of Intelligent Material Systems and Structures

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Carbon fibre reinforced plastic composites used in spacecraft structures are susceptible to delamination, debonds and fibre cracking that may arise during manufacturing, assembly or in-service operations (e.g. caused by debris impacts in near-Earth orbital spaceflights). Therefore, in situ and real-time health monitoring is necessary to avoid time-consuming and unsafe visual inspections performed either on-ground or during extra vehicular activities. In this article, a recently created ‘smart’ carbon fibre reinforced plastic composite structure with embedded piezoelectric lead zirconate titanate transducers was used to detect multiple areas of artificial delamination and real impact damage of different size using nonlinear ultrasound. The electrical insulation of embedded piezoelectric lead zirconate titanate transducers was achieved by interlacing a dry layer of woven glass fibre fabric between the sensor and the carbon fibre reinforced plastic plies before curing. Damage detection was successfully demonstrated using both second harmonic generation and nonlinear modulation (sidebands) of the measured ultrasonic spectrum. The material nonlinear response at the second harmonic and sidebands frequencies was also measured with a laser Doppler vibrometer to validate the nonlinear ultrasonic tests and provide damage localisation. Experimental results revealed that the proposed configuration of embedded piezoelectric lead zirconate titanate transducers can be utilised for on-board ultrasonic inspection of spacecraft composite parts.

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“…3) Blom's functional transformation followed by the Cornish-Fisher transformation [24]; 4) transformations from the Box-Cox-Tukey family (generalization of power and logarithmic transformations); 5) transformations based on the use of a priori information about the distribution of a random variable (approximation by χ 2 -distribution, the Fisher distribution, etc., lead to a normal distribution with transformation) [25]; 6) Johnson transform (approximation from a family of distributions followed by transformation into a Gaussian distribution) [26,27].…”

Section: Introductionmentioning

confidence: 99%

Research of Diagnostic Parameters of Composite Materials Using Johnson Distribution

Babak¹,

Eremenko²,

Zaporozhets³

2019

IJC

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In this paper, it was proposed to carry out a preliminary normalization of diagnostic parameters using the Johnson distribution, which with three basic distribution groups (SL, SB, SU), covers a wide class of empirical distributions. The mathematical description of the family allows us to find the approximating probability density function in an explicit form, to determine the distribution parameters for obtaining the corresponding function (curve), as well as the inverse function for finding the quantiles of the specified levels. To assess the accuracy of the obtained normalized data, they were compared with the data obtained by replacing the resulting law with a Gaussian one. Percentages of values were compared in the implementation under study, which concentrated in the limits of estimated quantiles. Implementations were obtained using the simulation method. By the same method, the correctness (relative systematic error) of determining the quantile values of the specified levels was evaluated. The error value δ was estimated between the conditionally true quantile value calculated from the generated pseudo-general complex and the value estimated using the methods considered in the paper. Obtained data show that the relative error in the calculation of quantiles using the Johnson distribution does not exceed 0.07% and decreases in two orders of magnitude than the currently accepted procedure for replacing sample laws with Gaussian.

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Solitary waves for Non-Destructive Testing applications: Delayed nonlinear time reversal signal processing optimization

Cited by 21 publications

References 21 publications

Signal-to-Noise Ratio analysis for time-reversal based imaging techniques in bounded domains

Signal-to-Noise Ratio analysis for time-reversal based imaging techniques in bounded domains

Nonlinear ultrasonic inspection of smart carbon fibre reinforced plastic composites with embedded piezoelectric lead zirconate titanate transducers for space applications

Research of Diagnostic Parameters of Composite Materials Using Johnson Distribution

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