Sparse signal representation and its applications in ultrasonic NDE

Zhang, Guangming; Chen, Ken; Harvey, David M.

doi:10.1016/j.ultras.2011.10.001

Cited by 98 publications

(41 citation statements)

References 71 publications

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“…Deconvolution can be divided into two minimization problems: i) "pure" deconvolution where signal is treated as convolution of test object reflectivity function with probing (reference) signal [10][11][12] and ii) reference signal production [18,19,23]. Deconvolution is the optimization problem that uses sparse deconvolution methods such as matching pursuit, Prony model or orthogonal matching pursuit in order to deconvolve the mixed data [5,6,15,16].…”

Section: Estimation Of the Parameters Of The Approximating Functionsmentioning

confidence: 99%

“…Furthermore, limited bandwidth is usually desirable, because signal acquisition system requirements can be relaxed [8], attenuation of ultrasound at high frequencies produces structural noise and signal degradation [9]. Deconvolution is used to address this problem [10][11][12]. It is based on assumption that reflections are sparse (based on usual defects structure which present themselves as layers or distributed cavities) and that every reflection is a time and amplitude translated copy of the original probing signal (reference).…”

Section: Introductionmentioning

confidence: 99%

“…Wiener filtering [13] can be used to address the problem, but it does not offer the decomposition of the signal into ToF and corresponding amplitudes. Iterative deconvolution techniques allow for efficient signal decomposition either processing one reflection at a time or several reflections simultaneously [6,10,12,15]. Yet, production of the reference signal constitutes a major problem in all deconvolution methods [5,6,16,17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Approximation performance of model functions for blind deconvolution of ultrasonic reflections

Lukoševičiūtė¹,

Drulyte²,

Svilainis³

2016

J. vibroeng.

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Abstract.Comparison of the approximation performance of the ultrasonic reflections by simple model functions used in blind deconvolution is presented. New model function was proposed where causality of the signal is easily modelled without the need for piecewise-linear equations. Parameter estimation technique was developed. Performance investigation was done for three model functions using four experimentally collected ultrasonic signals under different signal filtering conditions.

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Section: Estimation Of the Parameters Of The Approximating Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Approximation performance of model functions for blind deconvolution of ultrasonic reflections

Lukoševičiūtė¹,

Drulyte²,

Svilainis³

2016

J. vibroeng.

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show abstract

“…This limit has placed a fundamental requirement for high data storage if one is to infer high frequencies, as is the case in ultrasound NDT. Compression techniques such as Fourier and wavelet decompositions have successfully been applied to the data compression problem [1,2], and this could now be safely regarded as a standard task when considering the problem of data compression. Even though it is successful, such an approach still requires an initially large number of samples to be stored, if high frequencies are involved.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing for Direct Time of Flight Estimation in Ultrasound-based NDT

Fuentes

Worden

Antoniadou

et al. 2017

Structural Health Monitoring 2017

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This paper presents an approach for estimation of ultrasonic time-of-flight (TOF) within a Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) context. The presented method leverages recent advances in the field of Compressive Sensing (CS), which makes use of sparsity in a transform domain of a signal in order to reduce the number of samples required to store it. For this, the ultrasound signals are considered to be sparse in their autocorrelation domain and a method is suggested for building suitable basis functions, based on Hankel matrices, which transform a signal into its autocorrelation domain. It is shown how this can be combined with standard CS techniques in order to achieve very low error in TOF estimates with up to one-tenth of the original ultrasound samples.

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“…Widely used methods at the present time are split spectrum processing [3][4][5][6][7], wavelet method [8][9][10][11][12][13][14][15][16], the high resolution pursuit (HRP) [17][18], and the chirplet transform [19][20]. The results are presented in various forms, so a direct comparison is very difficult.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Flaw Detection in Composite Materials Using SSP-MPSD Algorithm

Benammar¹,

Drai²

2014

Journal of Electrical Engineering and Technology

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-Due to the inherent inhomogeneous and anisotropy nature of the composite materials, the detection of internal defects in these materials with non-destructive techniques is an important requirement both for quality checks during the production phase and in service inspection during maintenance operations. The estimation of the time-of-arrival (TOA) and/or time-of-flight (TOF) of the ultrasonic echoes is essential in ultrasonic non-destructive testing (NDT). In this paper, we used split-spectrum processing (SSP) combined with matching pursuit signal decomposition (MPSD) to develop a dedicated ultrasonic detection system. SSP algorithm is used for Signal-to-Noise Ratio (SNR) enhancement, and the MPSD algorithm is used to decompose backscattered signals into a linear expansion of chirplet echoes and estimate the chirplet parameters. Therefore, the combination of SSP and MPSD (SSP-MPSD) presents a powerful technique for ultrasonic NDT. The SSP algorithm is achieved by using Gaussian band pass filters. Then, MPSD algorithm uses the Maximum Likelihood Estimation. The good performance of the proposed method is experimentally verified using ultrasonic traces acquired from three specimens of carbon fibre reinforced polymer multi-layered composite materials (CFRP).

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Sparse signal representation and its applications in ultrasonic NDE

Cited by 98 publications

References 71 publications

Approximation performance of model functions for blind deconvolution of ultrasonic reflections

Approximation performance of model functions for blind deconvolution of ultrasonic reflections

Compressive Sensing for Direct Time of Flight Estimation in Ultrasound-based NDT

Ultrasonic Flaw Detection in Composite Materials Using SSP-MPSD Algorithm

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