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

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 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.

Research on an Ultrasonic Phased Array Defect Detection System

2013 ◽  
Vol 13 (8) ◽  
pp. 1376-1381 ◽  
Author(s):  
Jian Li ◽  
XiangLin Zhan ◽  
ShiLi Chen ◽  
ShiJiu Jin
Keyword(s):  
Defect Detection ◽  
Phased Array ◽  
Detection System ◽  
Ultrasonic Phased Array
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