• Journal of the Korean Society for Nondestructive Testing
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• v.17 no.4
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• pp.248-261
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• 1997

Ultrasonic pattern recognition is the most effective approach to the problem of discriminating types of flaws in weldments based on ultrasonic flaw signals. In spite of significant progress in the research on this methodology, it has not been widely used in many practical ultrasonic inspections of weldments in industry. Hence, for the convenient application of this approach in many practical situations, we develop an intelligent ultrasonic signature classification software which can discriminate types of flaws in weldments based on their ultrasonic signals using various tools in artificial intelligence such as neural networks. This software shows the excellent performance in an experimental problem where flaws in weldments are classified into two categories of cracks and non-cracks. This performance demonstrates the high possibility of this software as a practical tool for ultrasonic flaw classification in weldments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.515-522
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• 2021

In this paper, we present and analyze a method of applying a machine learning to ultrasonic test signals for constant monitoring of the welding faults in a tubular steel tower. For the machine learning, feature selection based on genetic algorithm and fault signal classification using a support vector machine have been used. In the feature selection, the peak value, histogram lower bound, and normal negative log-likelihood from 30 features are selected. Those features clearly indicate the difference of signals according to the depth of faults. In addition, as a result of applying the selected features to the support vector machine, it has been possible to perfectly distinguish between the regions with and without faults. Hence, it is expected that the results of this study will be useful in the development of an early detection system for fault growth based on ultrasonic signals and in the energy transmission related industries in the future.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.439-448
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• 2012

TFT-LCD panel images have non-uniform brightness, noise signal and defect signal. It is hard to divide defect signal because of non-uniform brightness and noise signal, so various divide methods have being developed. In this paper, we suggest method to divide defective regions on TFT-LCD panel image by estimating a menas of two different size of windows, which is suggested by Eikvil et al., and using difference of them. But in this method, the size of detectable defects is restricted by the size of window, hence it has inefficient problem that the size of window have to increase to divide a large defect region. To solve this problem we suggest an algorithm which can divide various size of defects, by using Multi-scale and restrict a detectable size of defects in each scale. To prove an efficiency of suggested algorithm, we show that resulting images of real TFT-LCD panel images and an artificial image with various defects.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.231-234
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• 2007

와전류를 이용한 전위강하법은 시험체에 와전류를 비 접촉식으로 발생 시킬 수 있어 기존의 방식에 비해 결함 검출시 발생하는 오차를 줄일 수 있고 물체의 결함 검출신호의 신뢰성을 향상 시킬 수 있다. 본 논문에서는 와전류를 이용한 전위강하법의 전자기 유한요소해석을 수행하였다. 3차원 유한요소해석과정에서 해석시간을 단축시키기 위해 각 영역별로 MVP, ESP, RMSP, TMSP의 미지수 변수를 부여하여 유한요소해석을 수행하였다. 기존의 전위강하법에 와전류의 개념을 적용하기 위해서 주파수에 대한 결함 깊이의 영향과 결함에 대한 주파수에 대한 영향을 검토해본 결과 결함의 깊이가 깊어질수록 또한 높은 주파수에서 신호의 크기가 선형적으로 증가하는 패턴을 보였다. 연구결과 와전류를 이용하여 ACPD법을 시행할 경우 데이터 처리 과정과 실험 장비를 단순화 시킬 수 있어 결함 검사를 손쉽게 할 수 있을 것이라 예상된다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.790_791
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• 2009

본 논문에서는 전자기 유한요소 해석을 통하여 원전 증기 발생기(SG, Steam Generator) 세관의 결함 변화에 따른 배열와전류프로브의 와전류탐상 특성을 해석하였다. 프로브의 전자기적 특성을 위해 맥스웰 방정식을 이용하여 지배방정식을 유도하였고, 이를 3차원 전자기 유한요소법을 이용하여 문제를 해석하였다. 해석을 위한 선정한 결함은 프로브의 특성파악을 위한 표준시험편과 원전 SG세관에 발생 가능한 결함인 Pitting, SCC, Wear, Multi SCC 결함을 선정하였다. 해석 대상으로는 원자력발전소 증기발생기 세관으로 사용되고 있는 Inconel 600 도체관을 사용하였다. 본 논문으로 통하여 결함의 형상, 크기, 시험주파수의 변화에 따른 탐상신호의 변화를 확인할 수 있었다. 본 논문의 결과는 배열와전류프로브의 와전류탐상 신호 평가시 도움이 될 것이다.

• Journal of the Korean Geotechnical Society
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• v.17 no.5
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• pp.97-105
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• 2001

현장타설말뚝의 건전도 평가에서 충격반향, 충격응답기법과 같은 비검측공 시험법을 적용하는 사례가 점차로 증가되는 추세에 있다. 비검측공시험법의 적용성을 체계적으로 평가하기 위해 수행된 일련의 연구결과 중에서 본 논문에서는 시험법의 결함탐지 능력을 평가하기 위해 수행된 해석적 연구결과에 관해 논한다. 이 연구에서는 시험법의 결함탐지능력에 대한 제반 영향인자 중에서 결함의 유형, 위치 및 크기의 영향을 평가하고자 하였다. 이를 위해서 여러 가지 조건에서 말뚝머리에 작용하는 충격하중에 대한 흙-말뚝 구조의 동적응답을 ABAQUS를 이용한 유한요소법으로 해석하였다. 이로부터 얻은 신호를 충격반향 혹은 충격응답기법으로 처리.분석하여 각 시험법의 결함탐지 능력을 평가해 보았다. 연구결과 이들 두 기법 모두 결함의 위치와 유형은 비교적 정확하게 탐지하는 것으로 나타났다. 그러나 충격응답기법을 이용하여 추정한 결함 크기는 신뢰도가 매우 낮았다. 이 문제를 해소하여 시험법의 적용성을 향상시키기 위해서는 측정된 동적응답신호를 이용하여 적절한 역계산 기법을 개발하여 적용할 필요가 있다는 결론을 얻었다.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.5
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• pp.536-542
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• 2011

Dissimilar metal welds in nuclear power plant are known as very susceptible to PWSCC flaws, and periodically inspected by the qualified inspector and qualified procedure during in-service inspection period. According to field survey data, the majority of their DMWs are located on tapered nozzle or adjacent to a tapered component. These types of configurations restrict examination access and also limit examination volume coverage. Additionally, circumferential scan for axially oriented flaw is very difficult to detect located on tapered surface because the transducer can't receive flaw response from reflector for miss-orientation. To overcome this miss-orientation, it is necessary adapt skewed ultrasonic transducer accomodate tapered surface. The skewed refracted longitudinal ultrasonic transducer designed by modeling and manufactured from the modelling result for axial flaw detection. Experimental results showed that the skewed refracted longitudinal ultrasonic transducer get higher flaw response than non-skewed refracted longitudinal ultrasonic transducer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.84-92
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• 2020

This paper describes the estimation method of cable fault location in rocket motors using M-sequence (Maximal Length Sequence). In order to estimate the location of a cable fault, three methods have been usually used: TDR (Time Domain Reflectometry), FDR (Frequency Domain Reflectometry), and TFDR (Time-Frequency Domain Reflectometry). However, these methods suffer the disadvantage of requiring users to be close to a test field, which is dangerous. The estimation method of cable fault location using M-sequence is proposed to solve this problem. The proposed method can make use of DAS (Data Acquisition System). The experiments were three cases: damaged, open, and short. The RG-58 coaxial cable was used in the experiments. As a result, the proposed method has better performance than that of conventional methods such as TDR and TFDR.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.55-62
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• 2014

This paper proposes a fault detection method utilizing converted images of acoustic emission signals and texture analysis for identifying bearing's multi-faults which frequently occur in an induction motor. The proposed method analyzes three texture features from the converted images of multi-faults: multi-faults image's entropy, homogeneity, and energy. These extracted features are then used as inputs of a fuzzy-ARTMAP to identify each multi-fault including outer-inner, inner-roller, and outer-roller. The experimental results using ten times trials indicate that the proposed method achieves 100% accuracy in the fault classification.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.17-24
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• 2014

This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well.