• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2002.05a
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• pp.391-394
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• 2002

제조물 책임법상의 결함은 설계, 제조 및 표시상의 결함으로 분류할 수 있다. 이 중 "표시상의 결함"이란 제조업자가 합리적인 설명·지시·경고 기타의 표시를 하였더라면 당해 제조물에 의하여 발생될 수 있는 피해나 위험을 줄이거나 피할 수 있었음에도 이를 하지 아니한 경우를 가리키며, 제품본체가 아무리 잘 만들어졌다 하더라도 제품의 안전과 관련된 정보나 설명이 올바로 제공되지 않았다면 제품은 결함이 있다고 간주됨을 의미한다.(중략)

• 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 Korean Society for Nondestructive Testing
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• v.19 no.6
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• pp.448-453
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• 1999

원전의 열성층 현상으로 발생하는 열 피로균열 및 입계응력부식균열(IGSCC) 등은 결함에 대해서 검사자의 특별한 관심과 노력 없이는 초음파를 이용해 이러한 종류의 결함검출 및 크기 측정이 쉽지 않다. 이러한 결함의 검출 및 크기 측정을 위해서 먼저 초음파 모드 변환 기법을 사용하여 결함 검출 및 결함 크기를 분류한 후에 결함 끝단에서의 초음파 회절파(tip diffraction)를 이용한 여러 가지의 초음파 기법 둥으로 정확한 결함 크기를 측정하여 가동전 중점검시 발견된 결함의 추적 관리 및 결함평가신뢰도 향상에 기여하고자 한다. 따라서, 여기서는 열 피로균열 및 입계응력부식균열 등과 같은 결함의 정확한 검출 및 크기 측정을 위해 초음파 모드 변환 기법의 특성을 철저히 이해하고 이에 관련된 초음파 신호들을 정확히 구분할 수 있는 방법을 기술하였다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.2
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• pp.169-177
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• 2017

In this paper, classification of spall and crack faults of gear teeth is studied by applying the ensemble empirical mode decomposition(EEMD) for the gear transmission error(TE). Finite element models of the gears with the two faults are built, and TE is obtained by simulation of the gears under loaded contact. EEMD is applied to the residuals of the TE which are the difference between the normal and faulty signal. From the result, the difference of spall and crack faults are clearly identified by the intrinsic mode functions(IMF). A simple test bed is installed to illustrate the approach, which consists of motor, brake and a pair of spur gears. Two gears are employed to obtain the TE for the normal, spalled, and cracked gears, and the type of the faults are separated by the same EEMD application process. In order to quantify the results, crest factors are applied to each IMF. Characteristics of spall and crack are well represented by the crest factors of the first and the third IMF, which are used as the feature signals. The classification is carried out using the Bayes decision theory using the feature signals acquired through the experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.10
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• pp.5123-5129
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• 2013

In this paper, it is proposed that JTAG fault injection environment and the results of the classification techniques that the reliability of embedded systems can be tested. As applying these, this is possible to quantitative analysis of vulnerable factor for system. The quantitative analysis for the degree of vulnerability of system is evaluated by faults errors, and failures classification schemes. When applying these schemes, it is possible to verify process and classify for fault that might occur in the system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.6
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• pp.94-101
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• 2007

In the Hard Disk Drive(HDD) production, the detect pattern or defective HDD set is important information to diagnosis of defective HDD set. This paper proposes a pattern recognition neural network for the defect distribution of HDD. In this paper, 5 characteristics are determined for the classification to six standard defect pattern classes. A multi-layer perceptron is trained for the pattern classification the inputs of which are 5 characteristic values and the 6 outputs are the nodes of standard patterns. The experiment with proposed neural network shows satisfactory results.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.75-86
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• 2015

Internet of Thing (IoT) computing is an environment where various devices with sensors and actuators are connect, and interact together to acquire contexts and provide useful services. With the advances of IoT technologies, its usability becomes an in important issue. However, there exist various types of faults in IoT computing which are not conventionally addressed in software research community. Providing reliable IoT services is challenging. In this paper, we present a hierarchy of IoT faults and analyze causes and symptoms of the faults. Based on the analysis, we define effective methods for managing IoT faults. We believe that our proposed framework for managing IoT faults can be utilized in reducing the development cost of IoT applications and enhancing the quality of the applications.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.1
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• pp.17-26
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• 1998

The specimen with the wear between a roller and a vane and a normal specimen are classified by AE signal pattern recognition method with a neural network classifier in airconditioning operation test. Also the specimen with the scoring between a shaft and a bearing and a normal specimen are classified by the same method. As the internal pressure increases, the wear between the roller and the vane increases. The different pairs of oils and refrigerants five the effect on the wear.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.1
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• pp.34-45
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• 1996

The signal pattern recognition program which can perform signal acquisition and processing, the extraction and selection of features, the classifier design and the evaluation, is developed and applied to the classification of artificial defects in the weld zone of Austenitic STS304. The neural network classifier is compared with the linear discriminant function classifier and the empirical Bayesian classifier. The signal through a broadband sensor is compared with that through a resonance type sensor. In recognition rate, the neural network classifier is best, and the signal through a broadband sensor is better.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.369-372
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• 2010

평판 디스플레이(flat panel display)의 크기가 커짐에 따라 다양한 기판을 이용한 제조 방법이 개발되고 있다. 디스플레이 제조 공정 중 기판의 결함을 찾아서 분류하는 검사 시스템은 최종 제품의 품질을 결정하는 매우 중요한 부분이다. 본 연구는 머신비전 기술을 이용하여 불투명하고 반사율이 높은 기판 표면의 결함을 찾아내고, 이 결함을 스크래치(scratch), 흑결함(dark defect), 백결함(white defect)으로 분류하는 장치를 구현하는데 목적이 있다. 이를 구현하기 위해 본 논문에서는 정밀 스테이지(stage)와 라인 카메라(line CCD camera)을 이용한 광학계를 활용하여 검사 시스템을 구현하였다. 구축된 시스템을 이용하여 취득한 이미지를 12 개의 영역으로 등분하여 각각의 국부 영역에 대한 문턱값 연산(thresholding)을 적용함으로써 조명의 불균일을 의한 검출 에러율을 획기적으로 낮추었다. 간단한 컴퓨터비전 알고리듬의 채용으로도 검사 시스템의 구현이 가능함을 보였다.