SOM and RBF Networks for Eddy Current Nondestructive Testing

2011 ◽  
Vol 219-220 ◽  
pp. 1093-1096 ◽  
Author(s):  
Bing Liu ◽  
Ai Hua Li ◽  
Chang Long Wang ◽  
Jian Bin Wang ◽  
Ye Teng Ni
Keyword(s):  
Nondestructive Testing ◽  
Eddy Current ◽  
Crack Detection ◽  
Detection Algorithm ◽  
Ratio Method ◽  
Test Scheme ◽  
Self Organizing Maps ◽  
Rbf Network ◽  
Fisher Ratio ◽  
Som Network

Eddy current testing is a popular nondestructive testing (NDT) technology with a solid theoretical foundation. This paper presents a new crack test scheme which uses a self-organizing maps (SOM) network and a radial basis function (RBF) network to process the crack feature signals in eddy current NDT. And Fisher ratio method is adopted to optimize the RBF network centers and simplifies the network structure. The validity of this crack detection algorithm is verified by an experiment in which the wave signals of different crack locations and depths are acquired from the simulations and used as the training and testing samples. Finally, the assessment of the network’s accuracy is performed and the result is satisfactory.

Electromagnetic methods in nondestructive testing of materials

2003 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Konstanty Gawrylczyk
Keyword(s):  
Sensitivity Analysis ◽  
Finite Elements ◽  
Nondestructive Testing ◽  
Eddy Current ◽  
Quality Evaluation ◽  
Leakage Flux ◽  
Electromagnetic Methods ◽  
Conducting Materials ◽  
Inductive Methods ◽  
Flux Probe

The article deals with progress in electromagnetic methods used for quality evaluation of conducting materials. The term "electromagnetic methods" covers the following areas: magneto-inductive methods, magnetic leakage flux probe method, magnetometer principle and eddy-current methods. For the aim of numerical cracks recognition the sensitivity analysis with finite elements was shown.

scholarly journals Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

2021 ◽  
Vol 11 (2) ◽  
pp. 813
Author(s):  
Shuai Teng ◽  
Zongchao Liu ◽  
Gongfa Chen ◽  
Li Cheng
Keyword(s):  
Feature Extraction ◽  
Crack Detection ◽  
Computational Cost ◽  
Concrete Structures ◽  
Detection Algorithm ◽  
Computational Time ◽  
Image Size ◽  
Important Indicator ◽  
Feature Extractor ◽  
Model Training

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.

Three Dimensional Detection of the via in the PCB CT Image Using Morphology Operation

2015 ◽  
Vol 752-753 ◽  
pp. 1406-1412
Author(s):  
Lei Zeng ◽  
Jian Chen ◽  
Han Ning Li ◽  
Bin Yan ◽  
Yi Fu Xu ◽  
...  
Keyword(s):  
Nondestructive Testing ◽  
Printed Circuit Board ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Circuit Board ◽  
Dimensional Structure ◽  
Three Dimension ◽  
Shape Information ◽  
Printed Circuit ◽  
Testing Technology

In modern industry, the nondestructive testing of printed circuit board (PCB) can prevent effectively the system failure and is becoming more and more important. As a vital part of the PCB, the via connects the devices, the components and the wires and plays a very important role for the connection of the circuits. With the development of testing technology, the nondestructive testing of the via extends from two dimension to three dimension in recent years. This paper proposes a three dimensional detection algorithm using morphology method to test the via. The proposed algorithm takes full advantage of the three dimensional structure and shape information of the via. We have used the proposed method to detect via from PCB images with different size and quality, and found the detection performances to be very encouraging.

FEA Design and Misfit Minimization for In-Depth Flaw Characterization in Metallic Plates With Eddy Current Nondestructive Testing

2009 ◽  
Vol 45 (3) ◽  
pp. 1506-1509 ◽  
Author(s):  
M. Cacciola ◽  
S. Calcagno ◽  
G. Megali ◽  
F.C. Morabito ◽  
D. Pellicano ◽  
...  
Keyword(s):  
Nondestructive Testing ◽  
Eddy Current ◽  
Flaw Characterization ◽  
Metallic Plates

scholarly journals The early stage wheel fatigue crack detection using eddy current pulsed thermography

2017 ◽  
Author(s):  
Jianping Peng ◽  
Kang Zhang ◽  
Kai Yang ◽  
Zhu He ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Fatigue Crack ◽  
Eddy Current ◽  
Crack Detection ◽  
Early Stage ◽  
Pulsed Thermography ◽  
Fatigue Crack Detection

Fatigue Crack Propagation Behavior and Degradation Characteristics of STS316L by Nondestructive Evaluation

2005 ◽  
Vol 297-300 ◽  
pp. 2016-2021
Author(s):  
So Soon Park ◽  
Seok Hwan Ahn ◽  
Chang Kwon Moon ◽  
Ki Woo Nam
Keyword(s):  
Crack Detection ◽  
New Technology ◽  
Signal Amplitude ◽  
Detection Algorithm ◽  
Source Location ◽  
Patch Type ◽  
Fatigue Crack Propagation Behavior ◽  
Existing Structures ◽  
Propagation Behavior ◽  
Evaluation Algorithm

Structural health monitoring (SHM) is a new technology that has been increasingly evaluated by the industry as a potential approach to improve the cost and ease of structural inspection. Piezoelectric smart active layer (SAL) sensor was fabricated to verify the applicability of finding cracks and conducting source location in a various materials. A crack detection and source location works were done in three kinds of test condition such as aluminum plates with crack for patch type SAL sensor, a smart airplane with embedding SAL sensor, and a concrete beam with real crack for practical application. From this experimental study, the evaluation algorithm for the arrival time delay and decrease of signal amplitude was suggested in this paper. Consequently, it was found that the SAL sensor and detection algorithm developed in this study can be effectively used to detect and monitor damages in the both existing structures and new designed smart structures.

Simulation and design of a superconducting eddy current probe for nondestructive testing

1998 ◽  
Vol 69 (2) ◽  
pp. 499-506 ◽  
Author(s):  
James R. Claycomb ◽  
Nilesh Tralshawala ◽  
Hsiao-Mei Cho ◽  
Mike Boyd ◽  
Zhongji Zou ◽  
...  
Keyword(s):  
Nondestructive Testing ◽  
Eddy Current
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