• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 한국추진공학회 2006년도 제27회 추계학술대회논문집
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• pp.289-292
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• 2006

In this paper, the hybrid algorithm of Support Vector Machine md Artificial Neural Network is used for the defect diagnostics algorithm for the aircraft turbo-shaft engine. The results of learning of ANN, especially, accuracy or speed of convergence are sensitive to the number of data, so a comparison between design point and off-design area, especially, Mach number and fuel flow variable area, is essential research. From application results for diagnostics of gas turbine engine, it was confirmed that the hybrid algorithm could detect well in the of-design area as well as design point.

• Journal of the Microelectronics and Packaging Society
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• 제30권1호
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• pp.30-41
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• 2023

With the aggressive down-scaling of advanced integrated circuits (ICs), electronic packages have become the bottleneck of both reliability and performance of whole electronic systems. In order to resolve the reliability issues, Institute of Electrical and Electronics Engineers (IEEE) laid down a roadmap on fault detection and diagnosis (FDD), thrusting the digital twin: a combination of reliability physics and artificial intelligence (AI). In this paper, we especially review research works regarding the signal-based FDD approaches on the electronic packages. We also discuss the research trend of FDD utilizing AI techniques.

• Transactions of the Korean Society of Mechanical Engineers A
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• 제38권2호
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• pp.205-210
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• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제21권11호
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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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• 제35권1호
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• pp.25-30
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• 2015

A wind turbine blade is an important component in wind-power generation, and is generally exposed to harsh environmental conditions. Ultrasonic inspection is mainly used to inspect such blades, but it has been difficult to quantify defect sizes in complicated composite structures. Recently, active infrared thermography has been widely studied for inspecting composite structures, in which thermal energy is applied to an object, and an infrared camera detects the energy emitted from it. In this paper, a calibration method for active optical lock-in thermography is proposed to quantify the size. Inclusion, debonding and wrinkle defects, created in a wind blade for 100 kW wind power generation, were all successfully detected using this method. In particular, a ${\phi}50.0mm$ debonding defect was sized with 98.0% accuracy.

• Journal of the Korean Society for Nondestructive Testing
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• 제1권1호
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• pp.6-12
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• 1981

Sizing of artificial flaws is accomplished by ultrasonic Satellite- Pulse Technigue(SPT). Negligible error is found.

• Journal of Korea Water Resources Association
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• 제55권spc1호
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2018년도 추계학술발표대회
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• pp.550-553
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• 2018

소프트웨어 시험 후 발견된 결함을 제거하기 위해서는 먼저 해당 결함의 위치를 정확히 찾아야 한다. 결함의 위치를 찾는 작업은 많은 양의 소스코드를 검토해야 하기 때문에 많은 노력을 요구한다. 해당 노력을 줄이기 위해 슬라이싱 기법, 스펙트텀 기법, 모델 기반 기법 등 많은 기법들이 연구되었다. 하지만 이들 연구들은 결함 위치로 추정한 탐색 영역의 범위가 넓어 결과의 효과가 떨어지는 단점이 있다. 그래서 결함 위치 추정의 정확도를 높이고 결함 위치 파악의 효과를 높이기 위해 본 논문은 프로그램 소스 코드 문장에 대한 시험 케이스의 커버리지 정보, 시험의 PAss/Fail 여부, Define-Use의 관계에 있는 문장 정보를 활용하여 각 문장의 결함 의심도를 산출하는 방법을 제안한다. 제안 방법을 실험을 통하여 확인한 결과, 낮은 지역화 비용으로 결함 위치 추정을 할 수 있었다.

• Journal of the Korean Society for Nondestructive Testing
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• 제29권2호
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• pp.108-113
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• 2009

MFL(magnetic flux leakage) inspection is a general method of non-destructive evaluation(NDE) of underground gas pipelines. Pipelines are magnetized by permanent magnets when MFL PIG(pipeline inspection gauge) gets through them. If defects or corrosions exist in pipelines, effective thickness is changed and thus variation of leakage flux occurs. The leakage flux signals detected by hall-sensors are analyzed to characterize defect's geometries such as length, width, depth, and so on. This paper presents several methods for estimating defect's length using MFL signals and their performances are compared for real defects carved in KOGAS pipeline simulation facility. It is found that 80% and 90% of minimum values for axial and peak values for radial signals respectively show the best performance in the point of length estimation error.

• Journal of Korea Foundry Society
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• 제43권1호
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• pp.31-36
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• 2023