• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.6
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• pp.551-558
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• 2013

A support vector machine (SVM) is a very powerful classification algorithm when a set of training data, each marked as belonging to one of several categories, is given. Therefore, SVM techniques have been used as one of the diagnostic tools in machine learning as well as in pattern recognition. In this paper, we present the results of classifying ball bearing fault types and severities using SVM with an optimized feature set based on the minimum distance rule. A feature set as an input for SVM includes twelve time-domain and nine frequency-domain features that are extracted from the measured vibration signals and their decomposed details and approximations with discrete wavelet transform. The vibration signals were obtained from a test rig to simulate various bearing fault conditions.

• Information Systems Review
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• v.6 no.2
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• pp.195-208
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• 2004

In this paper we propose a new software project development management model incorporating quality management to schedule management. Though many efficient techniques such as code review and inspection are used to remove defects, the effect of defect removal time on project schedule hasn't been studied much. However, poor quality management has an important effect upon overall schedule and sometimes software projects fails due to it. Thus, quality management and schedule management should be considered together and we need to reflect the time to maintain software quality into the schedule management. For the proposed model we introduced "Quality Value" representing the needed time to remove software defects. We assume PSP/TSP to gather the needed data for quality value. The proposed model can be used to predict the effect of software defects on schedule in advance and to prevent schedule lag.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.183-184
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• 2013

미국은 1960년대 초부터 제조물의 결함으로 인하여 발생한 손해에 대하여 판례나 학설을 통해 종래의 불법행위법의 범위를 확대하여 과실 계약법제가 엄격책임의 법제로 대체되어 엄격책임을 지게 하였다. 1970년대에 접어들면서 선진국들은 학설 판례에 맡기었던 제조물책임문제를 입법을 통하여 해결하고자 하였다. 즉 유럽국가들은 1985년 제조물책임에 관한 EC지침을 채택하고 1987년부터 대부분의 EU회원국들이 제조물책임법을 제정하였다. 우리나라도 제조물의 결함으로 인한 피해가 급증하면서 피해자와 직접적인 계약관계가 없는 제조자에게 계약책임을 지울 수 없다는 한계를 인식하고 피해자를 보호하기 위해 2000년에 제조물책임법을 입법하여 2002년부터 시행하였다. 동법의 핵심은 제조물책임에 관하여 과실의 유무를 묻지 않고 결함만을 책임요건으로 하는 무과실책임주의를 채택했다는 점이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.881-883
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• 2022

고객사에 납품하는 웨이퍼의 안정적인 공급을 위한 웨이퍼 엣지의 결함 검출 장비다. 본 연구에서는 OpenCV와 임베디드 시스템, 머신러닝, 전자 회로 그리고 센서/카메라 기술을 핵심 기술로 R&D 한다. 고객사에서 불량 웨이퍼 발생에 대응하기 위한 장비의 데이터를 생산하여 고객과의 신뢰도 향상 및 유지를 할 수 있다. 그리고 결함이 특정 공정 지점에서 발생하는지 탐색할 수 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.53-60
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• 2020

The bogies of railway vehicles are one of the most critical components for service. Fatigue defects in the bogie can be initiated for various reasons, such as material imperfection, welding defects, and unpredictable and excessive overloads during operation. To prevent the derailment of a railway vehicle, it is necessary to evaluate and detect the defect of a connection weldment between the car body and bogie accurately. The safety of the bogie weldment was checked using an ultrasonic test, and it is necessary to determine the occurrence of defects using a learning method. Recently, studies on deep learning have been performed to identify defects with a high recognition rate with respect to a fine and similar defect. In this paper, the databases of weldment specimens with artificial defects were constructed to detect the defect of a bogie weldment. The ultrasonic inspection using the wedge angle was performed to understand the detection ability of fatigue cracks. In addition, the convolutional neural network was applied to minimize human error during the inspection. The results showed that the defects of connection weldment between the car body and bogie could be classified with more than 99.98% accuracy using CNN, and the effectiveness can be verified in the case of an inspection.

• 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.

• Plant Journal
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• v.8 no.3
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• pp.49-54
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• 2012

In this study, the way how we can find the defects of motor windings in advance will be discussed. The magnetic flux spectrum in the high frequency region of the large motor was analyzed based on the actual fault practices related with motor windings. In case of defective motor relative amplitude ratio of the stator slot frequency to its sideband was very high compared to that of healthy motor. And the defective signal related with motor windings was indicated in advance in the magnetic flux spectrum prior to over 1 month before failure. Considering this aspect it can be estimated that magnetic flux spectrum in the high frequency region has the excellent predictive diagnostic capability.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2002.05a
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• pp.413-418
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• 2002

베어링은 산업현장에서 널리 쓰여지는 중요 부품이다. 따라서 이의 결함에 따른 손실을 예방하기 위해서는 이상을 진단하고 검지하는 기법이 요구된다. 따라서 본 연구에서는 저속회전하므로 노이즈가 많이 포함되어 절상상태의 신호검출이 어려운 저속회전베어링의 외륜이상을 웨이브렛의 Denoising 기법을 적용하여 정량적으로 진단하고 패턴인식법 중의 하나인 KDI(Kullback Discrimination Information)를 적용하여 이상상태의 진단/검지능력을 시험해 보았다. 웨이브랫의 Denoising 기법은 노이즈 캔셀링(Noise canceling)이 능력이 뛰어났고, HDI기법은 저속회전베어링의 정상과 이상의 분류에 뛰어난 검지능력이 있음을 알 수 있었다.(중략)

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1891-1892
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• 2008

본 논문에서는 부분방전 패턴인식을 위한 퍼지뉴럴네크워크(Fuzzy-Nueral Network를 설계한다. 퍼지뉴럴네트워크의 구조에서 규칙의 전반부는 개별적인 입력 공간을 분할하여 표현하고, 규칙의 후반부는 다항식으로서 표현되며 오류역전파 알고리즘을 이용하여 연결가중치인 후반부 다항식의 계수를 학습한다. 또한, 유전자 알고리즘을 이용하여 각 입력에 대한 전반부 멤버쉽함수의 정점과 학습률 및 모멤텀 계수를 최적으로 동조한다. 제안된 네트워크는 부분방전 패턴인식을 위해 다중 출력을 가지며, 초고압 XLPE 케이블 절연접속함의 모의결함에 대해 부분방전 신호를 패턴인식한다. 부분방전 신호는 PRPDA 방법을 통해 256개의 입력 벡터와 4개의 출력 벡터를 가지며, 보이드 방전, 코로나 방전, 표면 방전, 노이즈의 4개 클래스를 분류하며, 패턴인식률로서 결과를 분석한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.281-286
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• 2003

Wavelet Transform can effectively represent the local characteristics of a signal in the space-frequency domain. However, the feature vector extracted using wavelet transform is not translation invariant. This paper describes a new feature extraction method using wavelet transform, which is translation-invariant. Based on this translation-invariant feature extraction, the iris recognition method, based on this feature extraction method, is robust to noises. Experimentally, we show that the proposed method produces super performance in iris recognition.