Novel Noncontact Eddy Current Measurement of Electrical Conductivity

Wang, Cuiping; Fan, Mengbao; Cao, Binghua; Ye, Bo; Li, Wei

doi:10.1109/jsen.2018.2870676

Cited by 61 publications

(23 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the conductivity classification has been realized by many methodologies of electromagnetic induction such as magnetic induction spectroscopy [24], eddy current measurement [16], and so forth, none of these researches has considered samples with tilting surfaces. Our previous research has first utilized photoelectric sensors to obtain the tilting angle and then resorted to eddy current sensors to find the conductivity after the phase compensation [8].…”

Section: Conductivity Classification Techniquementioning

confidence: 99%

“…One of the intriguing conclusions is that the square of the phase of the impedance is proportional to the conductivity when the thickness of the sample is more than four times the skin depth [15]. This conclusion is also drawn in the form of logarithm [16]. In other words, the logarithm of the phase of the impedance is proportional to the logarithm of the conductivity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Conductivity Classification Technique for Non-Magnetic Tilting Metals by Eddy Current Sensors

Zhang

Yin

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The classification of conductivity is significant for recycling metallic scraps. The eddy current sensor is distinct from other classification methods by its merits of non-contact, economical setup , fast measuring, and so forth. By introducing a setup that could ensure the single-valued mutual inductance trajectory on the complex plane, we propose the circle fitting method to extract the global features of different trajectories. It is subsequently observed that the fitting circle centers-one of the global featuresfor the same conductivity with various tilting angles are distributed longitudinally close to each other. The feature line, which is parallel to the abscissa axis, is introduced to represent this gathering distribution behavior. The feature lines for different conductivity are distinguishable. Thus, we construct a simple classification method with only two steps. The test results show great fidelity of the proposed classification technique which can successfully classify the tilting metallic samples within 11.3 • .

show abstract

Section: Conductivity Classification Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Conductivity Classification Technique for Non-Magnetic Tilting Metals by Eddy Current Sensors

Zhang

Yin

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Compared with other detection methods, eddy current testing has many advantages in detecting metal components. It can detect metal component defects [7] and other properties [8,9], especially on the surface and subsurface damage of metal components, such as cracks, folding, pore, and inclusions [10,11].…”

Section: Introductionmentioning

confidence: 99%

Classification and Quantitative Evaluation of Eddy Current Based on Kernel-PCA and ELM for Defects in Metal Component

Deng

Bao

et al. 2019

Metals

Self Cite

View full text Add to dashboard Cite

Eddy current testing technology is widely used in the defect detection of metal components and the integrity evaluation of critical components. However, at present, the evaluation and analysis of defect signals are still mostly based on artificial evaluation. Therefore, the evaluation of defects is often subjectively affected by human factors, which may lead to a lack in objectivity, accuracy, and reliability. In this paper, the feature extraction of non-linear signals is carried out. First, using the kernel-based principal component analysis (KPCA) algorithm. Secondly, based on the feature vectors of defects, the classification of an extreme learning machine (ELM) for different defects is studied. Compared with traditional classifiers, such as artificial neural network (ANN) and support vector machine (SVM), the accuracy and rapidity of ELM are more advantageous. Based on the accurate classification of defects, the linear least-squares fitting is used to further quantitatively evaluate the defects. Finally, the experimental results have verified the effectiveness of the proposed method, which involves automatic defect classification and quantitative analysis.

show abstract

“…Conductivity, as a parameter of ferromagnetic materials, can serve as an index to sort different materials, and evaluate heat treatment condition or measure the stress of mechanical parts [1][2][3]. Due to the advances in electromagnetic nondestructive evaluation, pulsed eddy current (PEC) testing has become a feasible and preferred technique for measuring conductivity of materials in recent years [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Lu et al [1,10] found a conductivity lift-off invariance phenomenon and proposed an inverse problem method to determinate the conductivity and permeability of ferrite metallic plates. Wang et al [2] proposed an eddy current measurement method to determinate electrical conductivity of samples, which indicated that the logarithm of phase signature of impedance change basically varies linearly with electrical conductivity. Yu et al [11] observed a conductivity invariance phenomenon of eddy current non-destructive testing (NDT), and used it to estimate the magnetic permeability of metals without the influence of its conductivity.…”

Section: Introductionmentioning

confidence: 99%

A PEC Thrice Subtraction Method for Obtaining Permeability Invariance Feature in Conductivity Measurement of Ferromagnetic Samples

et al. 2019

Self Cite

View full text Add to dashboard Cite

Conductivity, as an important index of structural health monitoring, can be used to evaluate heat treatment condition, and sort different materials or measure the stress of mechanical parts. However, the permeability of a measured sample has significant impact on the detected signal in pulsed eddy current (PEC) testing, which is prone to measurement errors due to the effect of permeability change. In this paper, a thrice subtraction method is investigated and utilized to obtain a permeability invariance (PI) feature for reducing permeability effect in conductivity measurement of ferromagnetic samples. The thrice subtraction method is based on the PEC signals of sample and air, the difference signal between the difference PEC signal and its normalization signal, and the difference signal between the difference normalization signal and its standard deviation. In the thrice subtraction signals, the behavior of the obtained PI feature is analyzed by experiments and simulations. The results demonstrate that the thrice subtraction method is a practicable program and the PI feature is potential to measure the conductivity of ferromagnetic samples. The work reported in this paper provides an effective approach to obtain a PI feature for estimating the conductivity of ferromagnetic samples without a permeability effect.

show abstract

Novel Noncontact Eddy Current Measurement of Electrical Conductivity

Cited by 61 publications

References 28 publications

A Novel Conductivity Classification Technique for Non-Magnetic Tilting Metals by Eddy Current Sensors

A Novel Conductivity Classification Technique for Non-Magnetic Tilting Metals by Eddy Current Sensors

Classification and Quantitative Evaluation of Eddy Current Based on Kernel-PCA and ELM for Defects in Metal Component

A PEC Thrice Subtraction Method for Obtaining Permeability Invariance Feature in Conductivity Measurement of Ferromagnetic Samples

Contact Info

Product

Resources

About