scholarly journals Defect detection in conducting materials using eddy current testing techniques

2014 ◽  
Vol 11 (4) ◽  
pp. 535-549 ◽  
Author(s):  
Hartmut Brauer ◽  
Marek Ziolkowski ◽  
Hannes Toepfer
Keyword(s):  
Eddy Current ◽  
Eddy Current Testing ◽  
Internal Defects ◽  
Testing Technique ◽  
Defect Identification ◽  
Testing Conditions ◽  
Current Testing ◽  
Conducting Materials ◽  
Testing Techniques ◽  
Electrical Conductors

Lorentz force eddy current testing (LET) is a novel nondestructive testing technique which can be applied preferably to the identification of internal defects in nonmagnetic moving conductors. The LET is compared (similar testing conditions) with the classical eddy current testing (ECT). Numerical FEM simulations have been performed to analyze the measurements as well as the identification of internal defects in nonmagnetic conductors. The results are compared with measurements to test the feasibility of defect identification. Finally, the use of LET measurements to estimate of the electrical conductors under test are described as well.

scholarly journals Evaluation of Coating Thickness Using Lift-Off Insensitivity of Eddy Current Sensor

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 419
Author(s):  
Xiaobai Meng ◽  
Mingyang Lu ◽  
Wuliang Yin ◽  
Abdeldjalil Bennecer ◽  
Katherine J. Kirk
Keyword(s):  
Eddy Current ◽  
Coating Thickness ◽  
Thickness Measurement ◽  
Eddy Current Testing ◽  
Thickness Variation ◽  
Testing Technique ◽  
Current Testing ◽  
Thin Skin ◽  
Embedded Algorithms ◽  
Lift Off

Defect detection in ferromagnetic substrates is often hampered by nonmagnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of nonmagnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multifrequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3% for an extended lift-off range of up to 10 mm.

scholarly journals Evaluation of Coating Thickness Using Lift-Off Insensitivity of Eddy Current Sensor

2021 ◽  
Author(s):  
Xiaobai Meng ◽  
Mingyang Lu ◽  
Wuliang Yin ◽  
Abdeldjalil Bennecer ◽  
Katherine Kirk
Keyword(s):  
Eddy Current ◽  
Coating Thickness ◽  
Thickness Measurement ◽  
Eddy Current Testing ◽  
Thickness Variation ◽  
Testing Technique ◽  
Current Testing ◽  
Thin Skin ◽  
Embedded Algorithms ◽  
Lift Off

Defect detection in ferromagnetic substrates is often hampered by non-magnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of non-magnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multi-frequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3 % for an extended lift-off range of up to 10 mm.

scholarly journals Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Baoling Liu ◽  
Jun He ◽  
Xiaocui Yuan ◽  
Huiling Hu ◽  
Xuan Zeng ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Eddy Current ◽  
Computational Cost ◽  
Eddy Current Testing ◽  
Two Dimensional ◽  
Convolutional Network ◽  
Defect Identification ◽  
Current Testing ◽  
Pulsed Eddy Current ◽  
Deep Convolutional Network

Accurate and rapid defect identification based on pulsed eddy current testing (PECT) plays an important role in the structural integrity and health monitoring (SIHM) of in-service equipment in the renewable energy system. However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network (DCNN), such as large memory and high computational cost, an intelligent defect recognition pipeline based on the general Warblet transform (GWT) method and optimized two-dimensional (2-D) DCNN is proposed. The GWT method is used to convert the one-dimensional (1-D) PECT signal to a 2D grayscale image used as the input of 2D DCNN. A compound method is proposed to optimize the baseline VGG16, a well-known DCNN, from four aspects including reducing the input size, adding batch normalization layer (BN) after every convolutional layer(Conv) and fully connection layer (FC), simplifying the FCs, and removing unimportant filters in Convs so as to reduce memory and computational costs while improving accuracy. Through a pulsed eddy current testing (PECT) experiment considering interference factors including liftoff and noise, the following conclusion can be obtained. The time-frequency representation (TFR) obtained by the GWT method not only has excellent ability in terms of the transient component analysis but also is less affected by the reduction of image size; the proposed optimized DCNN can accurately identify defect types without manual feature extraction. And compared to the baseline VGG16, the accuracy obtained by the optimized DCNN is improved by 7%, to about 99.58%, and the memory and computational cost are reduced by 98%. Moreover, compared with other well-known DCNNs, such as GoogLeNet, Inception V3, ResNet50, and AlexNet, the optimized network has significant advantages in terms of accuracy and computational cost, too.

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