• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.697-699
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• 2002

In this paper, the characteristics analysis of the eddy current sensor by using boundary element method package is presented. For the boundary element analysis. Faraday, which is the commercial package of the integrated engineering software, is used. To observe the impedance characteristic of the eddy current senor with the sensor position and lift-off, the eddy current testing analysis is performed on the ferromagnetic plate with defect. Considering the skin depth of the ferromagnetic specimen, the 800(Hz) driving source is chosen. The result shows that electro motive force is reduced as the probe moves to near the defect and the lift-off of the probe increases.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1555-1560
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• 2004

We made a simplified eddy-current-tester, and observed some materials for the ingredients and mass and locations. The tester detects the current as frequency shifts of a LC-resonance circuit, which are caused by the eddy currents. Using air-wick coil and a multi-piled ceramic capacitor, we made a resonance system whose frequency was 100KHz. The shift quantity is few; so, to detect it, we used a frequency counter, and counted the shift. We can detect 10Hz order's shift.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.5
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• pp.393-397
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• 2007

Eddy current standard including an artificial slot for the calibration of absolute type surface probe was fabricated. Developed eddy current standard has the electric conductivity and dimensions, and contains artificial slot as established in ASTM E 1629. The width and depth of artificial slot are 0.1 mm and 0.5 mm respectively. This slot was only possible to measure the depth on the two side edges, and impossible for the middle part with general measurement tools. The ultrasonic test method was applied for measuring depth of the middle part of the artificial slot in the standard. Using this method the dimension could be measured successfully with uncertainty about $15\;{\mu}m$. Calibration of eddy current standard for the absolute probe can be performed by this technique.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.1
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• pp.8-14
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• 2004

Model-based inversion tools for eddy current signals have been developed by combining neural networks and finite element modeling, for quantitative flaw characterization in steam generator tubes. In the present work, interpretation of experimental eddy current signals was carried out in order to validate the developed inversion tools. A database was constructed using the synthetic flaw signals generated by the finite element model. The hybrid neural networks composed of a PNN classifier and BPNN size estimators were trained using the synthetic signals. Experimental eddy current signals were obtained from axisymmetric artificial flaws. Interpretation of flaw signals was conducted by feeding the experimental signals into the neural networks. The interpretation was excellent, which shows that the developed inversion tools would be applicable to the Interpretation of real eddy current signals.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.4
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• pp.288-294
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• 2016

This paper aims to compare the ability to detect notch defects existing in the plate and welded area using a flexible ECA (eddy current array) probe with OmniScan MX and MS-5800E. The characteristics of signals with various frequencies and lift-offs were also compared. As a result, when signals of frequencies 500, 1000, and 1500 kHz were used, the amplitude of the signal increased, as the depth of the notch increased, but reduced linearly in accordance with the lift-off variation. In addition, the detection sensitivity of the weld defect was found to be closely related to the contact surface of the probe and specimen. In this paper, it was demonstrated that the detection sensitivity was excellent when the contact surface of the probe and the specimen was sufficient, but it was poor when the contact surface was insufficient.

• Proceedings of the KIEE Conference
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• 1997.07a
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• pp.198-200
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• 1997

This paper consider a simple Nondestructive Testing(NDT) having eddy currnt effect. We analyzed the two dimension modeling of alternative magnetic field. eddy current with voltage source. And, the current magnitude and phase data obtained from each different frequency five object position is used for learning the neural network. Therefore, we can recognize an object position pattern from new input current magnitude, phase data.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.6
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• pp.622-634
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• 2003

This paper is concerned with a computational method for recovering a crack shape of steam generator tubes of nuclear plants. Problems on the shape identification are discussed arising in the characterization of a structural defect in a conductor using data of eddy current inspection. A surface defect on the generator tube ran be detected as a probe impedance trajectory by scanning a pancake type coil. First, a mathematical model of the inspection process is derived from the Maxwell's equation. Second, the input and output relation is given by the approximate model by virtue of the hybrid use of the finite element and boundary element method. In that model, the crack shape is characterized by the unknown coefficients of the B-spline function which approximates the crack shape geometry. Finally, a parameter estimation technique is proposed for recovering the crack shape using data from the probe coil. The computational experiments were successfully tested with the laboratory data.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.302-310
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• 2010

In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.

• Proceedings of the Korean Magnestics Society Conference
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• 1997.11a
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• pp.124-125
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• 1997

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.3
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• pp.254-259
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• 2014

A pressurized heavy water reactor (PHWR) core has 380 fuel channels contained and supported by a horizontal cylindrical vessel known as the calandria, whereas a pressurized water reactor (PWR) has only a single reactor vessel. The pressure tube, which is a pressure-retaining component, has a 103.4 mm inside diameter ${\times}$ 4.19 mm wall thickness, and is 6.36 m long, made of a zirconium alloy (Zr-2.5 wt% Nb). This provides support for the fuel while transporting the $D_2O$ heat-transfer fluid. The simple tubular geometry invites highly automated inspection, and good approach for all inspection. Similar to all nuclear heat-transfer pressure boundaries, the PHWR pressure tube requires a rigorous, periodic inspection to assess the reactor integrity in accordance with the Korea Nuclear Safety Committee law. Volumetric-based nondestructive evaluation (NDE) techniques utilizing ultrasonic and eddy current testing have been adopted for use in the periodic inspection of the fuel channel. The eddy current testing, as a supplemental NDE method to ultrasonic testing, is used to confirm the flaws primarily detected through ultrasonic testing, however, eddy current testing offers a significant advantage in that its ability to detect surface flaws is superior to that of ultrasonic testing. In this paper, effectiveness of flaw detection and the depth sizing capability by eddy current testing for the inside surface of a pressure tube, will be introduced. As a result of this examination, the ET technique is found to be useful only as a detection technique for defects because it can detect fine defects on the surface with high resolution. However, the ET technique is not recommended for use as a depth sizing method because it has a large degree of error for depth sizing.