• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.3
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• pp.193-199
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• 2015

Active thermography techniques have the capability of inspecting a broad range simultaneously. By evaluating the phase difference between the defected area and the healthy area, the technique indicates the qualitative location and size of the defect. Previously, the development of the defect detection method used a variety of materials and the test specimen was done. In this study, the proposed technique of lock-in is verified with artificial specimens that have different size and depth of subsurface defects. Finally, the defect detection capability was evaluated using comparisons of the phase image and the amplitude image according to the size and depth of defects.

• Journal of the Korean Institute of Gas
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• v.8 no.3 s.24
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• pp.31-36
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• 2004

Electrofusion(EF) joints have been widely used as they are easy to fuse and suitable for high-quality joints for polyethylene(PE) pipes. This paper studies the cause of defects and classifies 5 types of defects. The defect detection technique for electrofusion joints of polyethylene piping is utilized by the ultrasonic phased array technique to obtain ultrasonic images of electrofusion joints. Test sample joints have been designed and fabricated using artificial defects which were made using paper. Finally, we studied the condition of electrofusion in the field and analyzed the main causes of defects. And we classified the defect types as local lack of fusion, sand inclusion, voids or air inclusion, short stab, excess penetration or excess bead.

• Journal of Magnetics
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• v.22 no.1
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• pp.34-42
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• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1773-1780
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• 1996

KBANN (knowledge-based artificial neural network) combining the symbolic approach and the numerical approach has been shown to be more effective than other machine learning models. However KBANN doesn't have the theory refinement ability because the topology of network can't be altered dynamically. Although TopGen was proposed to extend the ability of KABNN in this respect, it also had some defects due to the link-ing of hidden nodes to input nodes and the use of beam search. The algorithm which could solve this TopGen's defects, by adding the hidden nodes linked to next layer nodes and using hill-climbing search with backtracking, is designed.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.337-343
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• 1996

In this paper, we investigate the defect detection by making use of pre-made reference image data and classify the defects by using the artificial neural network. The approach is composed of three main parts. The first step consists of a proper generation of two reference image data by using a low level morphological technique. The second step proceeds by performing three times logical bit operations between two ready-made reference images and just captured image to be tested. This results in defects image only. In the third step, by extracting four features from each detected defect, followed by assigning them into the input nodes of an already trained artificial neural network we can obtain a defect class corresponding to the features. All of the image data are formed in a bit level for the reduction of data size as well as time saving. Experimental results show that proposed algorithms are found to be effective for flexible defect detection, robust classification, and high speed process by adopting a simple logic operation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.788-794
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• 2009

To assess the condition of stator insulation, nondestructive tests were performed on twenty five coil groups and twenty six motors. The stator windings has nominal ratings of 6.6kV and are classified into five coil groups ;one group with healthy insulation and four groups with four different types of artificial defects. After completing nondestructive tests, the AC voltage applied to the stator windings was gradually increasing until insulation failure in order to obtain the breakdown voltage. No.1, No.2 and No.6 of 6.6kV motors failed near rated voltage of 14kV, 8.7kV and 14kV, respectively. The breakdown voltage of three motors was lower that expected for good quality coils(14.2kV) in 6.6kV motors. No.3 and No.6 of 4.16kV motors failed near rated voltage of 5.6kV and 4.2kV, respectively. Almost all of failures were located in a line-end coil at the exit from the core slot. The breakdown voltages and the types of defects showed strong relation to the stator insulation tests such as in the case of AC current, dissipation factor(tan${\delta}$) and partial discharge magnitude.

• Proceedings of the KIEE Conference
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• 1996.07c
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• pp.1774-1777
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• 1996

In this paper, applicability of SI-PD(switching impulse - partial discharge) testing method was put on an attempt as a newly proposed diagnostic method for the underground distribution power cable system in Korea. For this purpose, SI-PD testing equipment was designed, and tests were performed using artificial needle-type defects integrated into the 22.9 kV CN/CV cables in drder to prove its reliability. As a result, arc noises, generated from spark gap, were considerably decreased by use of a pneumatic switch immersed into oil, and artificial needle-type defects were well detected with impulse voltage level under $2U_0$. These results imply that it is likely possible to apply SI-PD measurement method as a the nondistructive test for the 22.9 kV CN/CV cable system in Korea.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.24 no.2
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• pp.305-316
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• 1994

The comparative detectability of the artificial defects among conventional radiographs, digital images and digital subtraction images was evaluated. The artificial defects were made within spogy bone of 24 unilateral mandibles of adult dogs. The results were as follows: 1. With normal exposure time, the detectability of digital subtraction radiographs was 90.3% which was statistically significant superior to those of conventional radiographs(78.0%) and digital images(75.9%) (p<0.05). 2. With half-exposure time, the detectability of conventional radiographs, digital images and digital subtraction radiographs was 68.4%, 67.3% and 69.9% respectively. There was no statistical significant difference among the detectability of these methods(p>0.05). 3. All radiographic images with normal exposure time showed statistically significant superior detectability to those with half-exposure time(p<0.05). 4. The detectability of digital subtraction radiographs was not linearly related to the standard deviation of the grey levels of reference line(p<0.05).

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.55 no.4
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• pp.178-182
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• 2006

Five model coils of 6.6 kV motor were manufactured with several defects. These stator coils have artificial defects such as void of groundwall insulation, removal of semi-conductive coating and damage of strand insulation. Epoxy-mica coupler(80 pF) was connected to five model coil terminals. The voltage applied to the coils was 3.81 kV, 4.76 kV, 6.0 kV and 6.6 kV, respectively. Partial discharge(PD) tests performed in the laboratory and shield room. Digital PD detector(PDD) and turbine generator analyzer(TGA) were used to measure PD activity. TGA summarizes each plot with two quantities such as the normalized quantity number(NQN) and the peak PD magnitude(Qm). The PD levels in pC were measured with PDD. PD patterns of model coils were indicated the internal and slot discharges. PD patterns are consistent with the result of measurement using PDD and TGA instruments. AC breakdown test was performed on five model coils in order to confirm the result of PD measurements. All the failures were located in a line-end coil at the exit from the core slot.

• Smart Structures and Systems
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• v.2 no.3
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• pp.253-274
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• 2006

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multidimensional analysis can provide excellent classification performance for notch-type defects in strands.