scholarly journals Development of Eddy Current Testing Probe for Thick-Walled Metal Plate and Quantitative Evaluation of Crackes.

2003 ◽  
Vol 69 (678) ◽  
pp. 455-462 ◽  
Author(s):  
Kazuhiko SATO ◽  
Haoyu HUANG ◽  
Tetsuya UCHIMOTO ◽  
Toshiyuki TAKAGI
Keyword(s):  
Quantitative Evaluation ◽  
Eddy Current ◽  
Metal Plate ◽  
Eddy Current Testing ◽  
Current Testing

scholarly journals Evaluation of Sensitivity of Remote Field Eddy Current Testing and Low-Frequency Eddy Current Testing for Inspecting Grooves of Metal Plate

2013 ◽  
Vol 54 (1) ◽  
pp. 90-95 ◽  
Author(s):  
Jing Wang ◽  
Noritaka Yusa ◽  
Hongliang Pan ◽  
Toshiyuki Takagi ◽  
Hidetoshi Hashizume
Keyword(s):  
Eddy Current ◽  
Low Frequency ◽  
Metal Plate ◽  
Eddy Current Testing ◽  
Current Testing

Sizing of natural crack using multi-output support vector regression method from multi-frequency eddy current testing signals

2020 ◽  
Vol 64 (1-4) ◽  
pp. 721-728
Author(s):  
Li Wang ◽  
Zhenmao Chen
Keyword(s):  
Support Vector Regression ◽  
Quantitative Evaluation ◽  
Eddy Current ◽  
Crack Depth ◽  
Eddy Current Testing ◽  
Support Vector ◽  
Efficient Operation ◽  
Current Testing ◽  
Multiple Parameters ◽  
Equipment Sizing

In the nondestructive evaluation for components of key equipment, sizing of natural crack is important in order to guarantee both the safety and efficient operation for large mechanical systems. Natural cracks have complex boundary and there may be electric current flowing through crack faces. If a simple model of artificial notch is used to simulate it, errors often occur in crack depth reconstruction from eddy current testing (ECT) signals. However, if a complex crack conductivity model is used, quantitative evaluation of natural crack will be transformed into a multivariable nonlinear optimization problem and the solution is difficult. In this paper, based on the relationship between crack parameters and features of multi-frequency ECT signals, a multi-output support vector regression algorithm using domain decomposition for parameters was proposed. The algorithm realized the quantitative evaluation of multiple parameters of crack in turn. Numerical examples with simulated and measured ECT signals were presented to verify the efficiency of the proposed strategy.

Quantitative evaluation of surface crack in ferromagnetic materials based on Bayesian network in eddy current testing

2020 ◽  
Vol 64 (1-4) ◽  
pp. 1073-1079
Author(s):  
Jing Song ◽  
Yanzhen Zhao
Keyword(s):  
Bayesian Network ◽  
Quantitative Evaluation ◽  
Eddy Current ◽  
Crack Size ◽  
Ferromagnetic Materials ◽  
Eddy Current Testing ◽  
Surface Cracks ◽  
Induced Voltage ◽  
Current Testing ◽  
Small Cracks

Quantitative evaluation of surface cracks using detection signal is of great significance for accurate prediction of cracks in eddy current testing. It is very difficult to evaluate both the width and depth of small cracks. A quantitative evaluation method based on Bayesian network is proposed for estimating the width and depth of surface cracks in the ferromagnetic materials. First, the simulation model of eddy current testing (ECT) is established and verified by the experimental results. Then, the variation of induced voltage with crack size is studied. Four feature points of real and imaginary part of induced voltage of the receiver coil are selected to characterize the crack size. Finally, a Bayesian network is applied to evaluate the crack size based on numerical simulation results. The evaluation results show that Bayesian network can accurately estimate the width and depth of small cracks.

Probabilistic evaluation of detection capability of eddy current testing to inspect pitting on a stainless steel clad using multiple signal features

2020 ◽  
Vol 64 (1-4) ◽  
pp. 47-55
Author(s):  
Takuma Tomizawa ◽  
Haicheng Song ◽  
Noritaka Yusa
Keyword(s):  
Stainless Steel ◽  
Eddy Current ◽  
Pressure Vessels ◽  
Eddy Current Testing ◽  
Probability Of Detection ◽  
Current Testing ◽  
Multiple Signal ◽  
Signal Features ◽  
Inner Surface ◽  
Drill Holes

This study proposes a probability of detection (POD) model to quantitatively evaluate the capability of eddy current testing to detect flaws on the inner surface of pressure vessels cladded by stainless steel and in the presence of high noise level. Welded plate samples with drill holes were prepared to simulate corrosion that typically appears on the inner surface of large-scale pressure vessels. The signals generated by the drill holes and the noise caused by the weld were examined using eddy current testing. A hit/miss-based POD model with multiple flaw parameters and multiple signal features was proposed to analyze the measured signals. It is shown that the proposed model is able to more reasonably characterize the detectability of eddy current signals compared to conventional models that consider a single signal feature.

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