• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.4
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• pp.422-429
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• 2002

The identification of ultrasonic flaw signals is a truly difficult task in the angle beam testing of welded joints due to non-relevant signals from the geometric reflectors such as weld roots and counter bores. This paper describes a new approach called "technique for identification of flaw signal using deconvolution(TIFD)" in order to identify the flaw signals in such a problematic situation. The concept of similarity function based on the deconvolution is introduced in the proposed approach. The "reference" signals from both flaws and geometric reflectors and test signals are acquired and normalized. The similarity functions are obtained by deconvolution of test signals with reference signals. The flaw signals could be identified by the patterns of similarity function. The initiative results show great potential of TIFD to distinguish notch comer signals from the geometric reflections.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.2
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• pp.131-143
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• 2013

Vibration condition monitoring of low-speed rotational slewing bearings is essential ever since it became necessary for a proper maintenance schedule that replaces the slewing bearings installed in massive machinery in the steel industry, among other applications. So far, acoustic emission(AE) is still the primary technique used for dealing with low-speed bearing cases. Few studies employed vibration analysis because the signal generated as a result of the impact between the rolling element and the natural defect spots at low rotational speeds is generally weak and sometimes buried in noise and other interference frequencies. In order to increase the impact energy, some researchers generate artificial defects with a predetermined length, width, and depth of crack on the inner or outer race surfaces. Consequently, the fault frequency of a particular fault is easy to identify. This paper presents the applications of empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) for measuring vibration signals slewing bearings running at a low rotational speed of 15 rpm. The natural vibration damage data used in this paper are obtained from a Korean industrial company. In this study, EEMD is used to support and clarify the results of the fast Fourier transform(FFT) in identifying bearing fault frequencies.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.4
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• pp.225-235
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• 2020

Ultrasonic investigation of damage detection has been widely used for non-destructive testing of various concrete structures. This study focuses on damage detection analysis with the aid of wave propagation in two-phase composite concrete with aggregate (inclusion) and mortar (matrix). To fabricate a realistic simulation model containing a variety of irregular aggregate shapes, the mesh generation technique using an image processing technique was proposed. Initially, the domains and boundaries of the aggregates were extracted from the digital image of a typical concrete cut-section. This enables two different domains: aggregates and mortar in heterogeneous concrete sections, and applied the grids onto these domains to discretize the model. Subsequently, finite element meshes are generated in terms of spatial and temporal requirements of the model size. For improved analysis results, all meshes are designed to be quadrilateral type, and an additional process is conducted to improve the mesh quality. With this simulation model, wave propagation analyses were conducted with a central frequency of 75 kHz of the Mexican hat incident wave. Several void damages, such as needle-shaped cracks and void-shaped holes, were artificially introduced in the model. Finally, various formats of internal damage were detected by implementing energy mapping based signal processing.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.501-510
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• 2000

Recently, advanced signal analysis which is called "time-frequency analysis" has been used widely in nondestructive evaluation applications. Wavelet transform(WT) and Wigner Distribution are the most advanced techniques for processing signals with time-varying spectra. Wavelet analysis method is an attractive technique for evaluation of material characterization nondestructively. Wavelet transform is applied to the time-frequency analysis of ultrasonic echo waveform obtained by an ultrasonic pulse-echo technique. In this study, the feasibility of noise suppression of ultrasonic flaw signal and frequency-dependent ultrasonic group velocity and attenuation coefficient using wavelet analysis of ultrasonic echo waveform have been verified experimentally. The Gabor function is adopted the analyzing wavelet. The wavelet analysis shows that the variations of ultrasonic group velocity and attenuation coefficient due to the change of material characterization can be evaluated at each frequency. Furthermore, to assure the enhancement of detectability and naw sizing performance, both computer simulated results and experimental measurements using wavelet signal processing are used to demonstrate the effectiveness of the noise suppression of ultrasonic flaw signal obtained from austenitic stainless steel weld including EDM notch.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.5
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• pp.383-392
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• 2007

In this paper, we study the prediction of depth and width of a defect in steam generator tube in nuclear power plant using neural network. To this end, we first generate eddy current testing (ECT) signals for 4 defect patterns of SG tube: I-In type, I-Out type, V-In type, and V-Out type. In particular, we generate 400 ECT signals for various widths and depths for each defect type by the numerical analysis program based on finite element modeling. From those generated ECT signals, we extract new feature vectors for the prediction of defect size, which include the angle between the two points where the maximum impedance and half the maximum impedance are achieved. Using the extracted feature vector, multi-layer perceptron with one hidden layer is used to predict the size of defects. Through the computer simulation study, it is shown that the proposed method achieves decent prediction performance in terms of maximum error and mean absolute percentage error (MAPE).

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.1
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• pp.132-137
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• 2006

The second part of paper related rotor failure is to evaluate that the axial magnetic flux measurement could be used as a tool of the condition monitoring system for the induction motor and to develope the diagnostic algorithm for the various fault in the electric motors. The magnetic leakage flux signal is captured by the flux coil located at the end of motor without the disturbance of the operation. And the signal is analyzed both time and frequency domain to detect the failure of the motor. Specific signature can be described in tin and frequency domain for each fault of the motor. The experimental test found that the rotor failures - broken rotor bar, broken end ing and rotor eccentricity, could be detected from the spectrum with high resolution. The method of detecting the rotor fault was found by analysing the specific frequency and the sideband of the rotor bar pass frequency from axial leakage flux spectrum. In addition the optimal flux coil and measuring equipment for the axial leakage flux measurement was verified and the diagnostic method for the detection of the rotor related failure was developed.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.11-19
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• 2013

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.15-16
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• 2016

본 논문에서는 케이블 결함과 위치를 검출하기 위한 SSTDR(spread spectrum domain reflectomerty) 기반의 진단 장치를 개발하였다. 개발된 진단 장치는 sstdr 기법의 검출 성능을 향상시키기 위해 시간-주파수 상관 분석을 이용하여 기준신호(reference signal)의 상관계수 최댓값 위치를 검출 한 후 기준신호를 제거하여 반사신호의 상관계수의 최댓값 위치를 검출하는 2단계 과정을 갖는다. 개발된 케이블 결함 진단 장치는 실증 시험장을 구축하고, 성능시험을 실시한 결과, ${\pm}1%$ 내 외의 오차를 갖는 것으로 확인되었다.

• Proceedings of the KIEE Conference
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• 2004.11d
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• pp.59-62
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• 2004

가스절연개폐장치에서 발생하는 고장을 조기에 예방하기 위해서는 방전신호를 분석하여 방전을 일으키는 결함을 유추하는 것이 핵심 기술이다. 본 논문에서는 UHF 대역에서 알고 있는 결함에 의한 방전신호를 측정하여 다양한 가시화 방법으로 방전유형을 분류, 정리함으로써 미지의 방전신호로부터 방전원인을 추정할 수 있도록 하였다.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.509-512
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• 2008

본 논문에서는 원전 증기발생기세관 진단을 위한 와전류탐상의 전자기 수치해석을 수행하였다. 전자기적 특성을 해석하기 위하여 맥스웰 방정식을 이용하여 지배방정식을 유도하였고, 3차원 전자기 유한요소 프로그램인 OPERA 3D를 이용하여 전자기 수치 해석을 수행하였다. 신호해석을 위해 사용된 프로브의 종류는 배열와전류프로브이며, FBH 결함의 신호를 해석하였다. 결함의 깊이는 세관 두께의 40[%], 60[%] 및 100[%]로 하였다. 시험주파수는 100[kHz], 300[kHz], 400[kHz]를 사용하였고, 각각의 결함 및 시험주파수에 대한 결과를 비교 분석하였다. 본 논문의 결과는 앞으로 배열와전류프로브를 이용하여 원전 증기발생기세관 진단을 할 경우 신호 해석에 도움이 될 것으로 사료된다.