Resolution Improvement of Ultrasonic Signals Using Sparse Deconvolution and Variational Mode Decomposition Algorithms

Benammar, Abdessalem; Chibane, Farid

doi:10.1134/s1061830920060029

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…For the signal, the degree of dispersion and concentration of the data is revealed by standard deviation. Therefore, an adaptive threshold condition is set for the dictionary D L by standard deviation, as shown in Equation (21).…”

Section: Signal Reconfigurationmentioning

confidence: 99%

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Ultrasonic Detection of Aliased Signal Separation Based on Adaptive Feature Dictionary and K–SVD Algorithm for Protective Coatings of Assembled Steel Structure

et al. 2023

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When using ultrasound to detect the thickness of protective coatings on assembled steel structures, the coatings are extremely thin, which can cause echo signals to overlap and impair the detection accuracy. Therefore, the study of the separation of the superimposed signals is essential for the precise measurement of the thickness of thinner coatings. A method for signal time domain feature extraction based on an adaptive feature dictionary and K–SVD is investigated. First, the wavelet transform, which is sensitive to singular signal values, is used to identify the extreme values of the signal and use them as the new signal to be processed. Then, the feature signal extracted by wavelet transform is transformed into Hankel matrix form, and the initial feature dictionary is constructed by period segmentation and random extraction. The optimized feature dictionary is subsequently obtained by enhancing the K–SVD algorithm. Finally, the time domain signal is reconstructed using the optimized feature dictionary. Simulations and experiments demonstrate that the method is more accurate in separating mixed signals and extracting signal time domain feature information than the conventional wavelet transform and Gabor dictionary-based MP algorithm, and that it is more advantageous in detecting the thickness of protective coatings.

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Section: Signal Reconfigurationmentioning

confidence: 99%

“…In recent years, sparse decomposition methods [20,21] have demonstrated remarkable advantages in signal aliasing problems. In the sparse decomposition method, the prototype atoms that match the original signal well are first screened.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Detection of Aliased Signal Separation Based on Adaptive Feature Dictionary and K–SVD Algorithm for Protective Coatings of Assembled Steel Structure

et al. 2023

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“…Also, some cases of lack of echo-signals from defects even with satisfactory quality of defectograms recording on the defective section of the track were found in the course of defectograms interpretation of detected acutely defective rails. It should be noted that various methods of signal and image processing are used when transcribing defectograms [12,13], but the results of measurements of the conventional dimensions of defects are obtained in manual mode. Despite the fact that the sensitivity of the corresponding channels of flaw detection equipment is set in advance and in some cases can exceed the sensitivity of acceptance control [14], the decrease in the conditional dimensions of defects to be detected will lead to a decrease in the reliability of ultrasonic testing.…”

Section: Introductionmentioning

confidence: 99%

Influence of speed of rail flaw detection on the reliability of its results

2023

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The system of non-destructive testing of rails during their operation on the railroad network of the Russian Federation is based on the use of removable and mobile means of flaw detection. The increase in the volume of control by mobile means in recent years is explained by their higher productivity due to the higher speed of control. But the increase in the speed of control inevitably leads to the loss of acoustic contact and missing defects.

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“…Bossmann [7] deconvolved the superimposed echo signal by removing the additive noise with a particular matching pursuit (MP) algorithm. The variational mode decomposition (VMD) algorithm was applied to deconvolute the signal by reducing the noise level, which increased the resolution of several defects in different positions [8]. Another class of methods is the minimum entropy deconvolution (MED) technique without acquiring prior assumptions, and the main idea is to find the inverse filter to make the output as sparse as possible [9,10].…”

Section: Introductionmentioning

confidence: 99%

Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application

Gao

Shi

et al. 2020

Sensors

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In the field of ultrasonic nondestructive testing (NDT), robust and accurate detection of defects is a challenging task because of the attenuation and noising of the ultrasonic wave from the structure. For determining the reflection characteristics representing the position and amplitude of ultrasonic detection signals, sparse blind deconvolution methods have been implemented to separate overlapping echoes when the ultrasonic transducer impulse response is unknown. This letter introduces the l1/l2 ratio regularization function to model the deconvolution as a nonconvex optimization problem. The initialization influences the accuracy of estimation and, for this purpose, the alternating direction method of multipliers (ADMM) combined with blind gain calibration is used to find the initial approximation to the real solution, given multiple observations in a joint sparsity case. The proximal alternating linearized minimization (PALM) algorithm is embedded in the iterate solution, in which the majorize-minimize (MM) approach accelerates convergence. Compared with conventional blind deconvolution algorithms, the proposed methods demonstrate the robustness and capability of separating overlapping echoes in the context of synthetic experiments.

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Resolution Improvement of Ultrasonic Signals Using Sparse Deconvolution and Variational Mode Decomposition Algorithms

Cited by 12 publications

References 17 publications

Ultrasonic Detection of Aliased Signal Separation Based on Adaptive Feature Dictionary and K–SVD Algorithm for Protective Coatings of Assembled Steel Structure

Ultrasonic Detection of Aliased Signal Separation Based on Adaptive Feature Dictionary and K–SVD Algorithm for Protective Coatings of Assembled Steel Structure

Influence of speed of rail flaw detection on the reliability of its results

Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application

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