Ultrasonic Phased Array Industrial Imaging Research with Compressed Sensing

2013 ◽  
Vol 347-350 ◽  
pp. 317-321 ◽  
Author(s):  
Xian Yue Ouyang ◽  
Guang Zhi Dai ◽  
Ren Fa Li ◽  
Qing Guang Zeng
Keyword(s):  
Compressed Sensing ◽  
Defect Detection ◽  
Phased Array ◽  
Signal To Noise Ratio ◽  
Sparse Sampling ◽  
Spectrum Estimation ◽  
Experiment System ◽  
Recovery Algorithm ◽  
Ultrasonic Phased Array ◽  
Focus Detection

this study presents an eight array ultrasonic signal phased array sparse sampling experiment system based ultrasonic phased array technology and Compressed Sensing (CS). Proposed system considers recovery ultrasonic beam signal received eight phased array elements with sparse samples captured using sub-Nyquist model in CS recovery algorithm. We have the block defect detection test in the system. The test result approximated the actual block defect position. Based on block defect detection test, We compared sparse sampling value using spectrum estimation to Compressed Sensing recovery algorithm imaging, and no focus and focus detection effect, proved the phased array experiment system based on Compressed Sensing .it can greatly improve the detection signal to noise ratio (SNR) and sensitivity. So we verify the phased array focus can improve the detection ability.

scholarly journals Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l0 Norm and Randomized Coordinate Descent

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 834
Author(s):  
Jin ◽  
Yang ◽  
Li ◽  
Liu
Keyword(s):  
Compressed Sensing ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Sparse Recovery ◽  
Coordinate Descent ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Recovery Algorithm ◽  
Core Concepts

Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory. In this paper, we proposed a sparse recovery algorithm using a smoothed l0 norm and a randomized coordinate descent (RCD), then applied it to sparse signal recovery and image denoising. We adopted a new strategy to express the (P0) problem approximately and put forward a sparse recovery algorithm using RCD. In the computer simulation experiments, we compared the performance of this algorithm to other typical methods. The results show that our algorithm possesses higher precision in sparse signal recovery. Moreover, it achieves higher signal to noise ratio (SNR) and faster convergence speed in image denoising.

Compressive sensing of phased array ultrasonic signal in defect detection: Simulation study and experimental verification

2017 ◽  
Vol 17 (3) ◽  
pp. 434-449 ◽  
Author(s):  
Zhiliang Bai ◽  
Shili Chen ◽  
Qiyang Xiao ◽  
Lecheng Jia ◽  
Yanbo Zhao ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Defect Detection ◽  
Phased Array ◽  
Electrical Discharge Machining ◽  
Optimal Algorithm ◽  
Greedy Algorithms ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Reconstruction Performance ◽  
Ultrasonic Phased Array

Ultrasonic phased array techniques are widely used for defect detection in structural health monitoring field. The increase in the element number, however, leads to larger amounts of data acquired and processed. Recently developed compressive sensing states that sparse signals may be accurately recovered from far fewer measurements, suggesting the possibility of breaking through the sampling limit of the Nyquist theorem. In light of this significant advantage, the novel use of the compressive sensing methodology for ultrasonic phased array in defect detection is proposed in this work. Based on CIVA software, we first present a simulated study on the effectiveness of the compressive sensing applied in ultrasonic phased array in defect detection through the average mean percent residual difference at varying compression rates. The results particularly show that the compressive sensing yields a breakthrough of the sampling limitation. We then experimentally demonstrate comparative analyses on the signals extracted from three types of artificial flaws (through-hole, flat-bottom hole, and electrical discharge machining notches) on two different specimens (made of aluminum and 20# steel). To find the optimal algorithm combination, the best sparse representation basis is chosen among fast Fourier transform, discrete cosine transform, and 34 wavelet kernels; the reconstruction performance is compared between five greedy algorithms; and the recovery accuracy is further improved via four sensing matrices selection. We also evaluate the influence of the sampling rate, and our results are comparable with the gold standard of signal compression, namely, the discrete wavelet transform.

Ultrasonic Phased Array Technique for Defect Detection and Sizing in Heavy-Walled Cast Components

2021 ◽  
Author(s):  
Md Shahjahan Hossain ◽  
Fadwa Dababneh ◽  
Russell Krenek ◽  
Hossein Taheri
Keyword(s):  
Defect Detection ◽  
Phased Array ◽  
Ultrasonic Phased Array

An orbital angular momentum acoustic metasurface for underwater defect detection

2021 ◽  
Author(s):  
Qiujun Ma ◽  
Chunyao Lu ◽  
Igor V. Minin ◽  
Oleg V. Minin ◽  
Kangyu Wang ◽  
...  
Keyword(s):  
Angular Momentum ◽  
Orbital Angular Momentum ◽  
Defect Detection ◽  
Phased Array ◽  
Simple Structure ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Reference Library ◽  
Reference Pattern

Abstract Fourier diffraction theorem can rapidly predict scattering characteristics of scatterers. This paper theoretically proposes Orbital Angular Momentum (OAM) to simplify the reference library in the underwater Fourier diffraction theorem. An acoustic metasurface is designed to replace the traditional phased array, meanwhile the underwater defect detection combining OAM and Fourier diffraction theorem is verified in the simulation. This acoustic metasurface has a high signal-to-noise ratio (SNR) when used for underwater defect detection. Compared with the traditional underwater defect detection method, the underwater defect detection method proposed in this paper has the advantages of simple structure and no reference pattern library.

Ultrasonic Phased Array Industrial Imaging with Sub-Nyquist Sampling Rate

2013 ◽  
Vol 347-350 ◽  
pp. 327-331
Author(s):  
Guang Zhi Dai ◽  
Guo Qiang Han ◽  
Xian Yue Ouyang
Keyword(s):  
High Speed ◽  
Phased Array ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Sampling Frequency ◽  
Frequency Resolution ◽  
Signal Frequency ◽  
Beam Focusing ◽  
Ultrasonic Phased Array

this paper uses a new type of FRI (Finite Rate of Innovation) sampling pattern based Sub-Nyquist sampling model breaked through Shannon theorem that it can get accurate signal reconstruction based on signal information rate, which requires the sampling frequency lower than two times the max signal frequency. We apply the new model in the ultrasonic phased array industrial imaging. In the experiment, ultrasonic phased array realized dynamic focusing and the high speed scan by ultrasonic array transducer of various array time delays to get flexible controllable synthesis beam composed signals that received by 32 phased array elements . The results indicate that in the model it greatly reduces the signal sampling frequency and improves the signal-to-noise ratio, frequency resolution at the same of the beam focusing and steering flexible.

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