Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique

Fan, Mengbao; Wang, Qi; Cao, Binghua; Ye, Bo; Sunny, Ali Imam; Tian, Gui Yun

doi:10.3390/s16050649

Cited by 30 publications

(16 citation statements)

References 43 publications

(48 reference statements)

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“…For a given conductive material, the frequency is not only concerned with the skin depth but also with the probe signal magnitude. An optimal frequency exists at which the maximum signal is retrieved for a fixed-size defect [27]. Figure 6 gives the simulated results by using the rotating probe excited by harmonic currents with the same density of 1.67 × 10 6 A/m 2 but different frequencies.…”

Section: Detection Of Arbitrary Orientation Defectsmentioning

confidence: 99%

Rotating Focused Field Eddy-Current Sensing for Arbitrary Orientation Defects Detection in Carbon Steel

Wang

Deng

2020

Sensors

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This paper presents a rotating focused field eddy-current (EC) sensing technique, which leverages the advantages of magnetic field focusing and rotating magnetic field, for arbitrary orientation defects detection. The sensor consists of four identical excitation coils orthogonally arranged in an upside-down pyramid configuration and a giant magneto-resistive (GMR) detection element. The four coils are connected to form two figure-8-shaped focusing sub-probes, which are fed by two identical harmonic currents with 90 degrees phase difference. A finite element model-based study of arbitrary orientation defects detection was performed to understand the probe operational characteristics and optimize its design parameters. Probe prototyping and experimental validation were also carried out on a carbon steel plate specimen with four prefabricated surface-breaking defects. In-situ spot inspection with the probe rotating above the defect and a manual line-scan inspection were both conducted. Results showed that the probe has the capability of detecting defects with any orientations while maintaining the same sensitivity and the defect depth can be quantitatively evaluated by using the signal amplitude. Compared with the existing rotating field probes, the presented probe does not require additional excitation adjustment or data fusion. Meanwhile, due to its focusing effect, it can generate a strong rotating magnetic field at the defect location with a weak background noise, thus yielding superior signal-to-noise ratio.

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Section: Detection Of Arbitrary Orientation Defectsmentioning

confidence: 99%

Rotating Focused Field Eddy-Current Sensing for Arbitrary Orientation Defects Detection in Carbon Steel

Wang

Deng

2020

Sensors

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“…Therefore, in this paper and the follow-up research, defect resistance signals will be used to automatically identify and classify defects. In addition, thanks to the single-hidden layer feedforward neural networks of ELM, it is superior to SVM in classification accuracy and generalization [30], and its training time is much lower than that of ANN [19,20]. In this paper, the length and depth of defects are fitted by the peak values of the resistance, reactance, and impedance of eddy current signals.…”

Section: Feature Extraction and Classificationmentioning

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

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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.

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“…The estimation of tube defect parameters performed with a high accuracy Fan et al 151 Both kernel principal component analysis (KPCA) and a support vector machine (SVM)…”

Section: Estimation and Classification The Geometrical Characteristicmentioning

confidence: 99%

Challenges in improving the performance of eddy current testing: Review

Abdalla

Faraj

Samsuri

et al. 2018

Measurement and Control

111

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Eddy current testing plays an important role in numerous industries, particularly in material coating, nuclear and oil and gas. However, the eddy current testing technique still needs to focus on the details of probe structure and its application. This paper presents an overview of eddy current testing technique and the probe structure design factors that affect the accuracy of crack detection. The first part focuses on the development of different types of eddy current testing probes and their advantages and disadvantages. A review of previous studies that examined testing samples, eddy current testing probe structures and a review of factors contributing to eddy current signals is also presented. The second part mainly comprised an in-depth discussion of the lift-off effect with particular consideration of ensuring that defects are correctly measured, and the eddy current testing probes are optimized. Finally, a comprehensive review of previous studies on the application of intelligent eddy current testing crack detection in non destructive eddy current testing is presented.

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Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique

Cited by 30 publications

References 43 publications

Rotating Focused Field Eddy-Current Sensing for Arbitrary Orientation Defects Detection in Carbon Steel

Rotating Focused Field Eddy-Current Sensing for Arbitrary Orientation Defects Detection in Carbon Steel

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

Challenges in improving the performance of eddy current testing: Review

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