Methods of Controlling Lift-Off in Conductivity Invariance Phenomenon for Eddy Current Testing

Jin, Zhongwen; Meng, Yuwei; Yu, Rongdong; Huang, Ruochen; Lü, Mingyang; Xu, Hanyang; Meng, Xiaobai; Zhao, Qian; Zhang, Zhijie; Peyton, Anthony; Yin, Wuliang

doi:10.1109/access.2020.3007216

Cited by 16 publications

(2 citation statements)

References 27 publications

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“…However, a preknowledge is required to select the efficient parameters that can effectively represent CUI [78]. Moreover, the ECT is sensitive to lift-off variations between the probe and the inspected surface, which degrades the depth penetration [79,80].…”

Section: Eddy Current Testingmentioning

confidence: 99%

Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review

et al. 2022

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Section: Eddy Current Testingmentioning

confidence: 99%

Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review

et al. 2022

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“…Besides, reducing number of turns of the sensor array is also in need for small-scale applications, for example in detecting defects on small bearing balls with small radius of 4 mm or less. Although promoting image stability, large number of turns will introduce thicker detection coil and introduce to relative lift-off which results in uncertainties in measurement [14,15]. The implementation of small number of turns has some barriers, such as low received signal value due to low coupling.…”

Section: Introductionmentioning

confidence: 99%

Improving SNR and Sensitivity for Low-Coupling EMT Sensors

Zhang

Chen

et al. 2023

IEEE Open J. Instrum. Meas.

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Electromagnetic tomography (EMT), also known as magnetic inductance tomography (MIT) is a tomographic modality widely employed in process industry and bio-medical applications. In particular, this technique plays an important role in imaging metallic objects since it can produce conductivity and permeability distributions in the region of interest. An EMT system consists of a coil array, a data acquisition system and an imaging reconstruction computer. Coils are used to generate electromagnetic field which interacts with the objects under investigation and measure the induced voltages. Conventionally, coils with sufficient inductance coupling (considerable number of turns or dimensions) are used to achieve high sensitivity and good SNR performance. However, this poses limitations for some applications, such as high temperature applications and small-scale facilities. In high temperature applications such as in steel or copper production processes, coils of large number of turns are more likely to be damaged due to breakdown of insulating materials between the turns, resulting in measuring errors. Besides, EMT applied in small-scale facility requires sensors with reduced dimensions, which results in weak magnetic coupling and lower SNR. In order to address these issues, this paper proposes a method to transform the impedance and hence increase the sensor signal level through designing boosting transformers. Simulation and experimental results suggest that this increases the system SNR and image stability.

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Research on Metallic Spheres Radius Classification Method Using Machine Learning With Eddy Current Testing

Zhang,

Li,

Zhao

et al. 2024

Int J Numerical Modelling

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Metallic spheres play a crucial role in industry and their accurate measurement is essential to ensure the safety of industrial production. Eddy current testing (ECT), which is non‐contact and non‐invasive, provides an efficient and precise approach for the parameter evaluation of metallic spheres. In this paper, we utilize machine learning (ML) methods to invert inductive signals in order to address the inverse problem of ECT, with the aim of reconstructing the radius of a metallic sphere. Datasets containing the radius information of the metallic sphere were constructed based on the simplified analytical solution. The datasets were divided into two parts based on the real part (RP) and imaginary part (IP) features, and the connection between the two features and the radius of the metallic sphere were compared by five classification models. While achieving accurate classification of aluminum and stainless steel spheres with different radius, the models are evaluated to ensure the reliability and validity of the models. The results show that the use of IP data as a classification feature has better accuracy as compared to RP. The K nearest neighbor (KNN) radius classifier has the highest accuracy of 95.5% in aluminum spheres and the random forest (RF) radius classifier has the highest accuracy of 95.9% in stainless steel spheres. In addition, all five classifiers are able to overcome the effect of lift‐off on the classification results.

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Methods of Controlling Lift-Off in Conductivity Invariance Phenomenon for Eddy Current Testing

Cited by 16 publications

References 27 publications

Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review

Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review

Improving SNR and Sensitivity for Low-Coupling EMT Sensors

Research on Metallic Spheres Radius Classification Method Using Machine Learning With Eddy Current Testing

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