scholarly journals Defect Classification Using Postpeak Value for Pulsed Eddy-Current Technique

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3390 ◽  
Author(s):  
Jiuhao Ge ◽  
Chenkai Yang ◽  
Ping Wang ◽  
Yongsheng Shi
Keyword(s):  
Defect Detection ◽  
Eddy Current ◽  
Surface Defect ◽  
Detection Experiment ◽  
Pulsed Eddy Current ◽  
Current Technique ◽  
Calculation Process ◽  
Surface Defect Detection ◽  
Better Than ◽  
Peak Value

In this paper, a feature termed as the postpeak value is proposed for the pulsed eddy current technique (PECT). Moreover, a method using the postpeak value is proposed to classify surface and reverse defects. A PECT system is built for verification purposes. Experiment results prove that the postpeak feature value has better performance than that of the traditional peak value in the case of reverse defect detection. In contrast, the peak value is better than the postpeak value in the case of surface defect detection. Experiment results also validate that the proposed classification algorithm has advantages: classification can be achieved in real time, the calculation process and results are easy to understand, and supervised training is unnecessary.

scholarly journals Surface Crack Detection for Carbon Fiber Reinforced Plastic Materials Using Pulsed Eddy Current Based on Rectangular Differential Probe

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jialong Wu ◽  
Deqiang Zhou ◽  
Jun Wang
Keyword(s):  
Carbon Fiber ◽  
Defect Detection ◽  
Eddy Current ◽  
Surface Defect ◽  
Fiber Reinforced ◽  
Fiber Reinforced Composite ◽  
Pulsed Eddy Current ◽  
Reinforced Composite ◽  
Carbon Fiber Reinforced ◽  
Current Detection

Aiming at the surface defect inspection of carbon fiber reinforced composite, the differential and the direct measurement finite element simulation models of pulsed eddy current flaw detection were built. The principle of differential pulsed eddy current detection was analyzed and the sensitivity of defect detection was compared through two kinds of measurements. The validity of simulation results was demonstrated by experiments. The simulation and experimental results show that the pulsed eddy current detection method based on rectangular differential probe can effectively improve the sensitivity of surface defect detection of carbon fiber reinforced composite material.

Pulsed eddy current technique for defect detection in aircraft riveted structures

2010 ◽  
Vol 43 (2) ◽  
pp. 176-181 ◽  
Author(s):  
Yunze He ◽  
Feilu Luo ◽  
Mengchun Pan ◽  
Feibing Weng ◽  
Xiangchao Hu ◽  
...  
Keyword(s):  
Defect Detection ◽  
Eddy Current ◽  
Pulsed Eddy Current ◽  
Current Technique

SDDNet: A Fast and Accurate Network for Surface Defect Detection

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Lisha Cui ◽  
Xiaoheng Jiang ◽  
Mingliang Xu ◽  
Wanqing Li ◽  
Pei Lv ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defect Detection

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

Sign in / Sign up

Export Citation Format

Share Document