Sparse Principal Component Thermography for Subsurface Defect Detection in Composite Products

2018 ◽  
Vol 14 (12) ◽  
pp. 5594-5600 ◽  
Author(s):  
Jin-Yi Wu ◽  
Stefano Sfarra ◽  
Yuan Yao
Keyword(s):  
Defect Detection ◽  
Principal Component ◽  
Subsurface Defect

Simultaneous subsurface defect detection and contact parameter assessment in a wheel–rail system

Wear ◽  
2008 ◽  
Vol 265 (11-12) ◽  
pp. 1837-1847 ◽  
Author(s):  
Massimiliano Pau ◽  
Bruno Leban ◽  
Antonio Baldi
Keyword(s):  
Defect Detection ◽  
Contact Parameter ◽  
Rail System ◽  
Subsurface Defect

3D Defect detection using weighted principal component thermography

2020 ◽  
Vol 128 ◽  
pp. 106039
Author(s):  
Seppe Sels ◽  
Boris Bogaerts ◽  
Simon Verspeek ◽  
Bart Ribbens ◽  
Gunther Steenackers ◽  
...  
Keyword(s):  
Defect Detection ◽  
Principal Component ◽  
Weighted Principal Component

Generative Principal Component Thermography for Enhanced Defect Detection and Analysis

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Kaixin Liu ◽  
Yingjie Li ◽  
Jianguo Yang ◽  
Yi Liu ◽  
Yuan Yao
Keyword(s):  
Defect Detection ◽  
Principal Component

Optimum eddy current excitation frequency for subsurface defect detection in SQUID based non-destructive evaluation

2010 ◽  
Vol 43 (8) ◽  
pp. 713-717 ◽  
Author(s):  
R. Nagendran ◽  
N. Thirumurugan ◽  
N. Chinnasamy ◽  
M.P. Janawadkar ◽  
R. Baskaran ◽  
...  
Keyword(s):  
Defect Detection ◽  
Eddy Current ◽  
Excitation Frequency ◽  
Non Destructive Evaluation ◽  
Subsurface Defect ◽  
Non Destructive

scholarly journals Defect Detection in Composite Products Based on Sparse Moving Window Principal Component Thermography

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jing Jie ◽  
Shiqing Dai ◽  
Beiping Hou ◽  
Miao Zhang ◽  
Le Zhou
Keyword(s):  
Defect Detection ◽  
Principal Component ◽  
Thermal Image ◽  
Moving Window ◽  
Detection Experiment ◽  
Pulsed Thermography ◽  
Detection Range ◽  
Fiber Reinforced Plastic ◽  
Reinforced Plastic ◽  
Free Region

As a nondestructive testing (NDT) technology, pulsed thermography (PT) has been widely used in the defect detection of the composite products due to its efficiency and large detection range. To enhance the distinction between defective and defect-free region and eliminate the influence of the measurement noise and nonuniform background of the thermal image generated by PT, a number of thermographic data analysis approaches have been proposed. However, these traditional methods only consider the correlations among the pixel while leave the time series correlations unmodeled. In this paper, a sparse moving window principal component thermography (SMWPCT) method is proposed to incorporate several thermal images using the moving window strategy. Also, the sparse trick is used to provide clearer and more interpretable results because of the structure sparsity. The effectiveness of the method is verified by the defect detection experiment of carbon fiber-reinforced plastic specimens.

Subsurface defect detection using phase evolution of line laser-generated Rayleigh waves

2020 ◽  
Vol 131 ◽  
pp. 106410 ◽  
Author(s):  
Dan Chen ◽  
Gaolong Lv ◽  
Shifeng Guo ◽  
Rui Zuo ◽  
Yanjun Liu ◽  
...  
Keyword(s):  
Defect Detection ◽  
Rayleigh Waves ◽  
Phase Evolution ◽  
Subsurface Defect
Sign in / Sign up

Export Citation Format

Share Document