Ultrasonic NDE of composite material structures using wavelet coefficients

Legendre, S.; Goyette, J.; Massicotte, Daniel

doi:10.1016/s0963-8695(00)00029-3

Cited by 41 publications

(23 citation statements)

References 8 publications

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“…This statement assumes that one has some knowledge of which coefficients in the representative basis play an important role in the signal. In ultrasonic NDT applications it has already been shown that using a wavelet basis can compress a typical signal by as much as 95% in terms of compression ratio [1,6]. However, there is a drawback to this approach, since the entire signal must be acquired, according to the Nyquist-Shannon theorem, for it to be then transformed into a sparse domain.…”

Section: Compressive Sensingmentioning

confidence: 99%

“…where Ψ is a basis function set, or dictionary, and β is the coefficient vector that represents the signal in the transform domain 1 . A good example of a sparse representation would be a sinusoid at a fixed frequency, which may contain a high number of points in the time domain, but may be fully represented by one complex coefficient in the Fourier domain.…”

Section: Sparsitymentioning

confidence: 99%

“…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%

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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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Section: Compressive Sensingmentioning

confidence: 99%

Section: Sparsitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…The selection of the mother wavelet to employ is not an easy task, because the number of parent wavelets (or mother wavelet), present in literature, are countless and their application the most various. However, the most used is the complex Morlet wavelet (Kareem and Kijewski, 2002;Legendre et al, 2001;Gilliam et al, 2000;Staszwski, 1997), because of its particularity of giving, at the same time, magnitude and phase information, very useful to visualize possible discontinuities. Moreover, this wavelet becomes very attractive for harmonic analysis, due to its analogies with the Fourier transforms expressed by this equation (Teolis, 1998) …”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

A new damage detection technique based on wave propagation for rails

Zumpano

Meo

2006

International Journal of Solids and Structures

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This paper presents a novel damage detection technique, tailored at the identification of structural surface damage on rail structures. The damage detection, proposed in this paper, exploits the wave propagation phenomena (P, S, Rayleigh and guided wave velocities) by identifying discrepancies, due to damage presence, in the dynamic behaviour of the structure. The uncorrelations are generated by waves reflected back to the sensor locations by the flaw surfaces. The peculiarity of the presented approach is the use of a time frequency coherence function for the identification of the arrivals of guided wave reflected back to the sensors by the damage surfaces.The damage detection methodology developed was divided into three steps. In the first step, the presence of the damage on the structure was assessed. In the second step, the arrival time of the reflected wave (or echo) was estimated using the continuous wavelet transform. Then, the detection algorithm was able, through a ray-tracing algorithm, to estimate the location of damage.A numerical investigation of two single damages was carried out. The damage was introduced on the railhead surface to simulate rolling contact fatigue defects. The results showed that the proposed methodology can be used successfully to localise the damage location, however, as expected, the localisation is strongly affected by the frequency range used. The results suggested that the separation and the characterisation of single modes are crucial for the identification of different types of rail defects. Further work is needed to establish the damage severity by relating the magnitude of the changes of the time frequency coherence to reflection and attenuation coefficients of each guided wave used and on the selection of the best range of frequency according to the type of damage to be identified.

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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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Ultrasonic NDE of composite material structures using wavelet coefficients

Cited by 41 publications

References 8 publications

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

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

A new damage detection technique based on wave propagation for rails

Ultrasonic Flaw Detection in Composite Materials Using SSP-MPSD Algorithm

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