• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.1
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• pp.45-52
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• 2016

Blast furnaces are crucial equipment for steel production. A typical furnace risks unexpected accidents caused by contraction and expansion of the walls under an environment of high temperature and pressure. In this study, an acoustic emission (AE) monitoring system was tested for evaluating the large-scale structural health of a blast furnace. Based on the growth of shell cracks with the emission of high energy levels, severe damage can be detected by monitoring increases in the AE energy parameter. Using this monitoring system, steel mill operators can establish a maintenance period, in which actual shell cracks can be verified by cross-checking the UT. From this study, we expect that AE systems permit early fault detection for structural health monitoring by establishing evaluation criteria based on the severity of shell cracking.

• Journal of IKEEE
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• v.26 no.2
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• pp.319-328
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• 2022

Bearing plays an important role in the operation of most machinery, Therefore, when a defect occurs in the bearing, a fatal defect throughout the machine is generated. In this reason, bearing defects should be detected early. In this paper, we describe a method using Convolutional Neural Networks (SN-CNNs) based on continuous wavelet transformations and Switchable normalization for bearing defect detection models. The accuracy of the model was measured using the Case Western Reserve University (CWRU) bearing dataset. In addition, batch normalization methods and spectrogram images are used to compare model performance. The proposed model achieved over 99% testing accuracy in CWRU dataset.

• Composites Research
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• v.28 no.4
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• pp.148-154
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• 2015

In order to detect damage in early stages and properly maintaining structures, the structural health monitoring technology is employed. In most cases, active-sensing SHM needs many piezoelectric (PZT) sensors and actuators. Thus, if there is a defect on PZT used for active-sensing SHM, the structural status could be misclassified. This study, for reliable SHM performance, investigated to detect defects of sensors by using the admittance-based sensor diagnostics. This study also introduced an algorithm that can diagnose sensor defects based only on data measured from the sensors in case that information about the changes in adhesive and environmental investigation, this study confirms that the proposed algorithm could be efficiently applied to real-world structures in which a significant temperature variation could take place.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.4
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• pp.369-375
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• 2012

In order to overcome the detection limit by the current nondestructive evaluation technology, a nonlinear resonant ultrasound spectroscopy(NRUS) technique was applied for detection of micro-scale cracks in a material. A down-shift of the resonance frequency and a variation of normalized amplitude of the resonance pattern were suggested as the nonlinear parameter for detection of micro-scale cracks in a materials. A natural-like crack were produced in a standard compact tension(CT) specimen by a low cycle fatigue test and the resonance patterns were acquired in each fatigue step. As the exciting voltage increases, a down-shift of resonance frequency were increases as well as the normalized amplitude decrease. This nonlinear effects were significant and even greater in the cracked specimen, but not observed in a intact specimen.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.3-5
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• 2022

본 연구는 VGGNet을 사용하여 광섬유 복합가공 지선 설비의 양/불량 판별을 수행한다. 광섬유 복합가공 지선이란, 전력선의 보호 및 전력 시설 간 통신을 담당하는 중요 설비로 고장 발생 전, 결함의 조기 발견 및 유지 관리가 중요하다. 현재 한국전력공사에서는 드론에서 촬영된 영상을 점검원이 이상 여부를 점검하는 방식이 주로 사용되고 있으나 이는 점검원의 숙련도, 경험에 따른 정확성 및 비용과 시간 측면에서 한계를 지니고 있다. 본 연구는 드론에서 촬영된 영상으로 VGGNet 기반의 양/불량 판정을 수행했다. 그 결과, 정확도 약 95.15%, 정밀도 약 96%, 재현율 약 95%, f1 score 약 95%의 성능을 확인하였다. 결과 확인 방법으로는 설명 가능한 인공지능(XAI) 알고리즘 중 하나인 Grad-CAM을 적용하였다. 이러한 광섬유 복합가공 지선 설비의 양/불량 판별은 점검원의 단순 작업에 대한 비용 및 점검 시간을 줄이며, 부가가치가 높은 업무에 집중할 수 있게 해준다. 또한, 고장 결함 발견에 있어서 객관적인 점검을 수행하기 때문에 일정한 점검 품질을 유지한다는 점에서 적용 가치가 있다.

• Korean Journal of Health Psychology
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• v.16 no.3
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• pp.557-575
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• 2011

This study tried to examine the characteristics of attention deficits in patients with Obstructive Sleep Apenea(OSA) with different age levels, and to examine which indices of polysomnograms might be related to the indices of attention deficits in OSAs. Two age-level groups and a normal control group were subjected to two computerized attention tests, including a continuous performance test(CPT) and a change blindness task(CBT). In addition, the three groups were subjected to a Polysomnography to extract several sub-indicators of polysomnogram, and an Epworth Sleepiness Scale which measures subjective sleepiness. As results, the OSAs showed significantly more omission and commission errors in CPT, and they showed lower accuracy in CBT compared to the normal group. The results of a correlational analysis showed that attention deficits in OSA are significantly correlated with arterial oxygen saturation among sub-indicators of polysomnograms. In conclusion, OSAs seems to be less attentive, having difficulties in response inhibition, and having deficiencies in noticing important environmental changes. Age seems to make these deficiencies even worse. Especially, the relationship between attention deficiency and hypoxia which could cause irreversible cerebrum damage has an implication in cognitive impairment prevention through early treatment.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.4
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• pp.330-335
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• 2013

Since real-time monitoring system like a fault early detection has been very important, infrared thermography technique as a new diagnosis method was proposed. This study is focused on the damage detection and temperature characteristic analysis of ball bearing using the non-destructive infrared thermography method. In this paper, for the reliability assessment, infrared experimental data were compared with the frequency data of the existing. As results, the temperature characteristics of ball bearing were analyzed under various loading conditions. Finally it was confirmed that the infrared technique was useful for real-time detection of the bearing damages.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.1
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• pp.61-67
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• 2015

Recently, the initiation of outside diameter stress corrosion cracking (ODSCC) at the tube support plate region of domestic steam generators (SG) with Alloy600 HTMA tubes has been increasing. As a result, SGs with Alloy600 HTMA tubes must be replaced early or are scheduled to be replaced prior to their designed lifetime. ODSCC is one of the biggest threats to the integrity of SG tubes. Therefore, the accurate evaluation of tube integrity to determine ODSCC is needed. Eddy current testing (ECT) is conducted periodically, and its results could be input as parameters for evaluating the integrity of SG tubes. The reliability of an ECT inspection system depends on the performance of the inspection technique and abilty of the analyst. The detection probability and ECT sizing error of degradation are considered to be the performance indices of a nondestructive evaluation (NDE) system. This paper introduces an optimized evaluation method for ECT, as well as the sizing error, including the analyst performance. This study was based on the results of a round robin program in which 10 inspection analysts from 5 different companies participated. The analysis of ECT sizing results was performed using a linear regression model relating the true defect size data to the measured ECT size data.